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Running head: Fish conservation areas for Michigan, USA 1
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Systematic planning of fish conservation focal areas for rivers of Michigan, USA 8
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Peter Esselman (Corresponding author) 12
Department of Zoology 13
Michigan State University 14
203 Natural Sciences Bldg. 15
East Lansing, MI 48824 16
Tel 517-432-1927 17
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Minako Edgar 20
Institute for Fisheries Research 21
University of Michigan and Michigan Department of Natural Resources 22
1109 N University Avenue. 23
Ann Arbor, MI 48109 24
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Jason Breck 26
Institute for Fisheries Research 27
University of Michigan and Michigan Department of Natural Resources 28
1109 N University Ave. 29
Ann Arbor, MI 48109 30
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Liz Hay-Chmielewski 33
Institute for Fisheries Research 34
Michigan Department of Natural Resources and University of Michigan 35
1109 N University Avenue. 36
Ann Arbor, MI 48109 37
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Lizhu Wang 39
Institute for Fisheries Research 40
Michigan Department of Natural Resources and University of Michigan 41
212 Museum Annex Building 42
1109 N University Avenue. 43
Ann Arbor, MI 48109 44
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Abstract 1
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In response to increased natural resource uses by human population growth and per capital 3
demand, government and non-government agencies must become more effective at the 4
practice of biological conservation. Landscape spatial planning to identify focal areas for 5
conservation of fishes is an important step to targeted site-level interventions to protect or 6
restore fish habitats, because it can improve the precision with which conservation efforts are 7
implemented. Computerized spatial planning algorithms that identify groups of adjacent 8
planning units with high species richness and good habitat quality indicate favorable conditions 9
for fish protection. Here we employed a commonly used systematic planning software, Marxan, 10
in conjunction with previously published fish range and human disturbance predictions to define 11
a network of conservation focal areas for rivers in Michigan focusing on large-bodied species, 12
small-bodied species, species of greatest conservation need, and all species together. 13
Depending on the scenario, we identified networks comprising between 15 and 20% of Michigan 14
stream length in over 1,000 focal areas ranging from 5 to 12 km in average length. Mean focal 15
area sizes were much larger for the Upper Peninsula than the Lower Peninsula. Approximately 16
40% of the focal areas defined flowed through already protected lands, but less than 10% had 17
upstream watersheds that were secured in protected areas. There was a 50% overlap in the 18
focal areas selected for the large- and small-bodied fish and SGCN. These areas may serve as 19
appropriate core areas for fish conservation strategies. Our maps show locations with a high 20
natural potential to conserve all of Michigan’s native fish species, and can serve as a reference 21
point for regionalization of comprehensive statewide planning efforts for fish conservation. We 22
recommend that our study is followed up with additional planning steps to define complementary 23
conservation zones to protect the functionality of ecosystems to support fishes, and to 24
implement watershed management practices upstream of focal areas. 25
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Keywords: Marxan, systematic conservation planning, connectivity, river system, fish 28
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Introduction 1
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Fisheries resource protection is a national priority in the United States because of the high 3
economic and social value of fish to society. Nonetheless, many U.S. fish species are depleted 4
or locally extirpated in portions of their ranges due to human activities that degrade habitats, 5
facilitate non-indigenous species invasions, and lead to overharvest (Ricciardi and Rasmussen 6
1999, Allan et al. 2005). Human induced stresses on fish populations are widespread in North 7
America leading to predictable declines in fish biodiversity, with 39% of freshwater and 8
anadromous species considered threatened or imperiled (Jelks et al. 2008). In response, 9
federal and state agencies continue to intensify their efforts to protect key stocks and species, 10
and have an increasing need of more effective tools that are spatially well-targeted for reversing 11
the trend of decline (Limburg et al. 2011). 12
13
Landscape spatial planning has become an important precursor to targeted site-level 14
interventions to protect or restore fish habitats. The early stages of spatial planning involve 15
spatial assessments of the fish distribution and abundance and the range of activities affecting 16
them, followed by a process of goal-setting and deciding among alternative management 17
scenarios (Gosselink et al. 1990, Sessions et al. 1997). Maps can be powerful aids to the 18
planning process because they allow for easy communication of information that can facilitate 19
dialogue. Assessment results frequently allow for mapping of places with high quality habitats 20
and desirable fish species. Assessment maps can then be used to select networks of fish 21
conservation focal areas—the locations of specific freshwater features requiring protection 22
(Abell et al. 2007). By identifying high quality habitats and important stocks, focal areas can 23
improve the precision with which conservation and protection efforts can be implemented, 24
presumably leading to gains in project success and resource-use efficiency. While professional 25
judgment can be a valuable source of information for defining focal areas (e.g., (Heiner et al. 26
2011), as the spatial extent of planning expands so too does the ecological complexity of the 27
planning area (Wu 2004) making it less effective for professional judgment alone to define an 28
effect network of conservation sites. At larger spatial extents, the planning process can be 29
greatly assisted by computerized planning algorithms that search for the minimum set of sites 30
that meet user-defined conservation goals (Sarkar et al. 2006). 31
32
Spatial planning algorithms have been developed to implement principals of protected area 33
design in a process known as “systematic conservation planning”. Design principals may 34
include a reserve network’s ability to represent the full diversity of freshwater features of interest 35
(“comprehensiveness”); its ability to guarantee the long term persistence of target biodiversity 36
features (“adequacy”); or its ability to achieve the above goals with limited cost to stakeholders 37
(“efficiency”) (Margules and Pressey 2000). Systematic conservation planning commonly 38
involves mapping of habitat conditions and/or species or stocks of interest, setting quantitative 39
targets for the amount of a feature to include in the network, and setting algorithmic parameters 40
to yield focal area networks with desirable qualities (Sarkar et al. 2006). The algorithms seek 41
groups of adjacent planning units where the data indicate favorable conditions for fish 42
protection. End products include maps of the minimum habitat area or river length needed to 43
meet the user-defined criteria for spatial network creation. While these maps have commonly 44
been used for defining protected area networks, the maps can also be interpreted as the 45
locations of conservation action sites in the event that full protection is infeasible or undesirable. 46
47
Though popularized in marine and terrestrial environments, systematic planning has recently 48
gained much momentum in rivers (Collier 2011). Rivers habitats are strongly influenced by 49
longitudinal connectivity to up- and downstream portions of the drainage network (Hynes 1975, 50
Poff 1997, Meyer and Wallace 2001, Stoms et al. 2005), leading to special systematic planning 51
4
considerations for these habitats. For instance, protecting a fish population in a river reach may 1
involve interventions to mitigate stressors in geographically distant habitats (Lake 1980, Skelton 2
et al. 1995, Pringle 1997, Moyle and Randall 1998). Further, the river boundary creates a hard 3
barrier to fish movement, enforcing strong connectivity limitations on individual fish dispersal. 4
Effective planning of riverine focal areas must accommodate spatial disparities in stressor 5
origins and network connectivity and other spatial considerations specific to rivers. 6
7
In this research, we applied a systematic planning algorithm to define a network of fish 8
conservation focal areas for rivers in Michigan. Our goal was to identify a set of high-quality 9
habitats with a high likelihood to support viable populations of native and social-economically 10
important fish species. As inputs to our analysis we used previously published predictive 11
models of fish species presence (Steen et al. 2008), and a riverine index of anthropogenic 12
stress to river habitats (Wang et al. 2008). This paper described the datasets used, illustrated 13
the methods for applying the planning software, and discussed our methods and results relative 14
to other published studies. 15
16
Methods 17
Study area 18
Water is the feature of Michigan’s physical geography with direct contact to Lake Erie, Lake 19
Huron, Lake Michigan and Lake Superior, and a glacially influenced landscape that is drained 20
by approximately 58,000 km of streams and rivers. The state is divided into Upper and Lower 21
Peninsulas by the connection between Lake Michigan and Lake Huron, with elevational ranges 22
from 174 masl to 603 masl, very few areas of high gradient areas, and many rivers that are 23
connected to lakes and wetlands. Many rivers in Michigan are characterized by high 24
groundwater discharge and stable flows due high precipitation infiltration into coarse grained 25
glacial tills and moraine deposits. The native fish community consists of 74 species (Smith et al. 26
2010). Important non-native fisheries include Chinook and coho salmon, rainbow trout, and 27
brown trout. The federal and state Wildlife Action Plan programs identified fishes that are 28
“species of greatest conservation need” (SGCN, refrence). The SGCN list includes 18 species 29
that are thought to be declining in their distribution and abundance in Michigan. 30
31
Datasets 32
For planning purposes, we used the Michigan Hydrography Database (MHD) that was derived 33
from the 1:24,000 scale National Hydrography Database (USGS 2009). For the MHD, the 34
original geometry and river lines of NHD were modified to remove duplicate stream reaches 35
(arcs), merge small non-interrupted adjacent arcs, and correct other data quality issues. We 36
used river reaches as basic planning units for aggregation into focal areas. A river reach in the 37
MHD is a section of stream that extends between two channel junctures, comprising a stream 38
origin, confluences with another channel, or junction with a lake, reservoir, or estuary. There 39
are xxx river reaches in the MHD. 40
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Each river reach in our database was attributed with additional variables to assist our planning 42
process. First, previously published geographic ranges of 82 native and non-native fishes were 43
mapped onto reaches (Appendix 1). The fish species range predictions were made from 44
landscape predictors using statistical models in conjunction with an extensive fish collection and 45
habitat dataset (Steen et al. 2008). Second, we used a previously published human disturbance 46
index, which scores each 1:100,000 NHD river reach from 0 to 100 (lowest to highest relative 47
disturbance) according to the intensity of landscape disturbance in the catchments of each 48
reach (Wang et al. 2008). Twenty-seven landscape disturbances summarized at network and 49
local catchment scales were combined into this index and included measures of disturbance 50
from land use, population density, transportation, nutrient enrichment, agricultural pollutants, 51
5
and point sources of pollution. Each disturbance factor was weighted according to its ability to 1
describe variation in a fish index of biotic integrity and percent intolerant species captured at 2
sites throughout Michigan. Small tributaries in the 1:24,000 scale MHD that did not overlap with 3
the 1:100,000 scale human disturbance scores were assigned null values. Because the human 4
disturbance index and fish data covered streams but not lakes, it was necessary to correct for 5
spatial gaps in data attribution caused by the presence of lakes in the stream network. We 6
applied an algorithm that attributed the artificial paths through lakes with the values of the 7
nearest upstream river segment. Finally, we attributed each reach with information about 8
whether or not it fell within a protected area and whether the reach had a protected upstream 9
catchment, which we defined as having 90% or more of its watershed area in protection. We 10
used the Conservation and Recreational Lands (CARL) database (DU and TNC 2007), which 11
contains information about all conservation-oriented lands from strictly protected ands to 12
localized extraction of multiple uses. 13
14
Systematic planning with Marxan 15
We used Marxan conservation planning software (Ball and Possingham 2000, Possingham et 16
al. 2000) to select a network of fish conservation focal areas. Marxan uses stochastic 17
optimization routines to generate spatial reserve systems that meet user-set goals for 18
biodiversity representation in the solutions while simultaneously addressing multiple other 19
conservation objectives. Representation targets controlling the amount of habitat to be included 20
in the reserve solution are set individually for each species. Representation targets are typically 21
set as either a set length or proportion of occupied habitat. Other conservation objectives that 22
can be accommodated by Marxan include the ability to control clumping of adjacent planning 23
units to create many small or fewer large focal areas and the ability to include habitats that are 24
already in protection. 25
26
Each Marxan run defines a different optimal configuration of planning units, because the 27
algorithm involves a random component that leads to slightly different solutions each time. In 28
most cases, the user runs the algorithm multiple times to either select the “best” solution, or to 29
select planning units based on the frequency with which they were selected for inclusion in a 30
network across all runs. Each run consists of a user-specified number of iterations. In each 31
iteration, a new planning unit is added to, retained, or dropped from the reserve based on its 32
effect on an objective measure of network cost. The Marxan cost function that is typically used 33
is: 34
35
Total cost = ∑Unit Cost + ∑(Boundary length * BLM) + ∑(species targets not achieved * SPF) 36
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where total cost is the objective function to be minimized; unit cost is a measure of human 38
disturbance intensity; boundary length is a cost determined by the outer boundary of the 39
selected focal areas; and species penalties are costs imposed for failing to meet representation 40
targets. The boundary length modifier (BLM) and the species penalty factor (SPF) are 41
coefficients that allow the user to control clumping and the strictness with which representation 42
goals are enforced in the Marxan solution. In combination, the different terms in the total cost 43
equation act to avoid more disturbed habitats (unit cost term), to ensure that target 44
representation is met (species penalties), and to select a focal area network with a high edge-to-45
interior ratio (boundary length). The “best” solution is the one that most closely meets species 46
representation goals with the minimum total cost. 47
48
We modified the Marxan cost function to aggregate stream reaches (arcs) and their junction 49
points rather than catchment polygons or octagons that have been commonly used. We 50
reformulated the total cost function as follows: 51
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Total cost = ∑(Unit Cost x reach length) + ∑(End points * EPM) + ∑(species targets not 2
achieved * SPF) 3
4
As above, unit cost is a measure of relative risk of human disturbance to habitats, in this case 5
from the estimates of Wang et al. (2008). We multiplied unit cost by reach length (km) to 6
eliminate a bias toward selection of longer reaches in the case where two reaches have the 7
same unit cost and the same species present. We replaced boundary length with the number 8
end points—channel junctions at the periphery of the focal area including tributary confluences, 9
upstream origin, and junctions with downstream river reaches. We also replaced the BLM with 10
an end point modifier (EPM) that served the same purpose. We reasoned that end points are 11
places where the focal area is vulnerable to negative upstream or downstream influences, and 12
that a focal area with a low end point-to-length ratio is more secure from threats. As end point 13
cost (end points x end point modifier) increases, more clumping of streams will occur during 14
focal area selection, making the network more compact. Species penalties are the same in both 15
equations. 16
17
After exploratory analyses to adjust coefficients (see Setting coefficients below), we 18
implemented four Marxan scenarios to determine fish conservation focal areas for the following 19
species groups: all fish species combined; large-bodied fishes; small-bodied fishes; and 20
Michigan SGCN taxa. Four solutions were generated to give resource managers added 21
information for decision making, and to learn from the results of different approaches. Large- 22
and small-bodied species tend to have much different spatial requirements, with large-bodied 23
fishes tending to require greater habitat areas to meet their life history requirements (Minns 24
1995). Group membership was assigned by the authors based on familiarity with the Michigan 25
fauna. SGCN species have more stringent requirements for protection, and thus were logically 26
separated for one of the scenarios. To generate focal area networks that corresponded well to 27
existing protected lands, we constrained each Marxan run to select its initial set of planning 28
units from rivers in catchments that are greater than 90% protected. This leads to a bias in the 29
final focal area networks toward areas with high pre-existing levels of protection. 30
31
Species representation targets 32
In setting species representation targets, we reasoned that species with limited ranges were at 33
the greatest risk of statewide extirpation, and that these species should have higher 34
representation in the focal area network to promote their long-term persistence. To implement 35
this logic, we used a sliding representation scale ranging from 40% of range to 0% of range, and 36
apportioned a value to each species according to its habitat occupancy. For each species we 37
calculated the proportion of available habitat predicted to be occupied by the modeled fishes 38
(Steen et al. 2008). We then excluded ubiquitous species with occupancies greater than 46,000 39
km (of 58,000 km total). We also excluded common carp and sea lamprey as they are nuisance 40
species and are managed for exclusion. We then calculated representation (R) for each 41
species as follows: 42
43
×
−−−= 4040
MinMax
MinOccR i
i 44
45
where Occi is the occupancy of species i, Min is the minimum occupancy across all species, 46
Max is the maximum occupancy across all species. The resulting representation targets (Figure 47
2; Appendix 1) were inversely related to relative occupancy rates, and were used to set 48
representation in all of the Marxan scenarios. 49
7
1
Setting coefficients 2
Before running Marxan it was necessary to set the EPM and SPF for each species, and to 3
specify the number of runs and iterations per run. We systematically tested the influence of the 4
EPM on total focal area length, total number of endpoints in the network, and average focal area 5
size (km) by running Marxan repeatedly (20 runs of 1,000,000 iterations) with different values of 6
EPM at powers of 10 from 10-4 to 109. The total focal area length was plotted against end points 7
of the best solution at each value of EPM (Figure 3). This plot was interpreted to identify the 8
point where both end points and total length could be simultaneously minimized, which was 9
determined to lie between and EPM of 100 and 1000. Further increase in the EPM beyond this 10
would have yielded relatively small reductions in the number of end junction points with 11
concordant large increases in total network length. We set the EPM equal to 500 and set the 12
species penalty factor arbitrarily large (SPF = 10,000) for all species to force Marxan to meet all 13
representation targets. 14
15
Complex optimization problems like ours require many iterations to achieve repeatable results 16
across runs. After exploring different numbers of iterations per run, we found that a very large 17
number of iterations—1010 per run—led to the same reaches being selected with high 18
frequency, indicating that runs were converging on similar solutions. Each of our four final 19
solutions was the least-cost solution of 100 runs with 1010 iterations. 20
21
Results 22
Marxan was used to generate focal area networks for each species group scenario. In all 23
scenarios, the representation goals for all species were met or exceeded (Appendix 1). Median 24
representation levels were 127% of the specified goal for the all-species scenario, 125% for the 25
large-bodied scenario, 134% for the small-bodied scenario, and 118% for the SGCN scenario. 26
For species with large ranges (high habitat occupancy and low representation goals), goals 27
were greatly exceeded, sometimes by up to 10 times (Appendix 1). For species with small 28
range sizes and high representation goals, Marxan selected sufficient habitat length so that just 29
over 100% of the representation goal was included. 30
31
The best solution for the all-species scenario identified 20% (18,411 km) of Michigan’s total 32
stream length that contains higher quality habitat patches and a high diversity of fish species 33
(Table 1). The best solution for large-bodied fishes identified 17.7% of Michigan stream length, 34
followed by the small-bodied group (15.9%), and the group with the fewest species in it, the 35
SGCN taxa (14.8%). Nearly 1,500 focal areas were selected in the all-species scenario, with an 36
average contiguous length of 11.6 km per focal area and a maximum length of 749 km. Focal 37
areas for large-bodied fishes were fewest in frequency (n = 1,070), and had the smallest 38
average length (5.6 km) of all solutions. Focal areas for SGCN were intermediate in frequency 39
(n = 1,242) and intermediate average length (11.6 km). Focal areas for small-bodied species 40
had intermediate frequencies (n = 1,148) and the largest average length (12.6 km) (Table 1). 41
Though the average focal area lengths ranged from 5 to 12 km, a few focal areas were 42
substantially larger than this in each scenario. The largest recommended focal area among all 43
scenarios was an 869 km patch of habitat for the small-bodied species group in the Manistique 44
River basin in north-central Michigan. The largest focal areas in the other scenarios were 749 45
km for the all species scenario, 495 km for large-bodied species, and 403 km for the SGCN 46
fishes. 47
48
Large-bodied fish and SGCN focal areas tended to be located on larger rivers with scattered 49
patches in smaller streams of the eastern Upper Peninsula and coastal drainages of Lake 50
Superior. The all-species and small-bodied species solutions contained large rivers and smaller 51
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streams in their focal areas. An analysis of variance (ANOVA) for the species group scenarios 1
showed significant differences in the mean length weighted mean stream order of focal areas in 2
each solution (F(2, 3441) = 4.18, p<0.05). Tukey’s HSD tests showed that SGCN focal area 3
streams had a significantly higher length weighted mean stream order than the small-bodied fish 4
streams (p<0.05), and that large-bodied fish focal areas did not differ significantly from the other 5
scenarios. 6
7
The spatial distributions of focal areas reflected fish species ranges, human disturbance 8
patterns, and the distribution of protected lands. The majority of large focal areas were located 9
in the Upper Peninsula where human population densities are generally low and where large 10
blocks of protected and undeveloped lands are located (Figure 4). Indeed, the mean size of 11
focal areas was significantly greater in the Upper Peninsula than the Lower Peninsula in all 12
scenario (t-tests, p<0.001). The mean size of focal areas in the all-species scenario in the 13
Upper Peninsula was 28 km versus 9 km for the Lower Peninsula. The mean focal area sizes 14
for the C in the other scenarios were 34 km for large-bodied species, 44 km for small-bodied 15
species, and 19 km for the SGCN species, compared to 10 km, 9 km, and 9 km for the same 16
groupings in the Lower Peninsula. Depending on the scenarios, from 36.7 to 43.3% of the 17
selected focal areas flow through currently protected lands (Table 1; Figure 5a). The majority of 18
the protected focal areas are located in the Lower Peninsula. However, very few focal areas lie 19
in protected catchments; defined here as having greater than 90% protected lands (Figure 5b). 20
The average percentage of focal area catchments in protection ranged from 66% to 71% 21
depending on the scenarios (Table 1). 22
23
There is a high degree of spatial overlap between the all-species scenarios versus the 24
scenarios run for the other species groups. More than 73% of the length of focal areas selected 25
for each species group solution was also selected in the all-species solution (Appendix 2), 26
suggesting that the same high quality areas are selected regardless of what grouping of taxa 27
was used. Focal area length-weighted mean disturbance scores differed significantly as a 28
function of the scenario run (F(3, 4910) = 12.19, P< 0.0001). The Tukey HSD procedure 29
revealed that mean disturbance scores were significantly lower for the SGCN scenarios than the 30
all-species scenario, and that significant differences did not exist between the other scenarios. 31
A comparison of spatial overlap between the species group solutions (with the all species 32
solution excluded) showed that small-bodied and SGCN focal areas have just over 50% overlap 33
with one another, and large-bodied focal areas are less than 50% contained in the other 34
scenarios. An overlay map of the three fish-group solutions (Figure 5) shows that much of the 35
overlap between SGCN and small-bodied species occurs in tributaries to main stem rivers in the 36
south central portion of the Lower Peninsula. This portion of the state has the highest levels of 37
fish diversity (Figure 1), driven in large part by the presence of small bodied species that require 38
warmer climate. The three fish-group scenarios shared the same 4,666 km of stream length in 39
common. 40
41
Discussion 42
Effective landscape planning of conservation activities is an important precursor to efforts to 43
improve the conservation status of extant populations (Sanderson et al. 2002). A landscape 44
scale perspective should allow resource management agencies to allocate their capacities 45
directly to places with a high probability to support viable populations for species of interest. 46
Though the spatial planning procedures employed for this study are consistent with freshwater 47
protected area planning, we did not approach this as a protected area planning effort, nor do we 48
recommend that all focal areas identified be put into protection. Rather, we encourage that our 49
results be interpreted as the locations with the highest natural potential for conserving all of 50
Michigan’s native and game fish species, whether conservation is achieved through direct 51
9
protection via land acquisition, easements, or legislative mechanisms, or through other actions 1
like riparian forest and wetland protection, implementation of agricultural and forestry best 2
management practices, or invasive species control. Our results can be seen as a reference 3
point for regionalization of comprehensive statewide planning efforts for fish conservation. 4
5
A comparison of the four scenarios that we ran offers insights into the planning algorithm, and 6
provides managers with flexibility to select focal areas that serve different species groups. Our 7
results show that planning for discrete assemblages of similar species yields slightly different 8
results than optimizing simultaneously for all species. Approximately 25% of the focal area 9
length of each species group solution was not shared with the all species solution (Table 2; 10
Appendix 2). Our finding that optimizing for smaller groups of species led to lower average 11
human disturbance levels (for SGCN, and for the small-bodied species, though non-significant) 12
suggests that optimizing for more species may pull solutions toward areas with high numbers of 13
species present at the expense of unit cost. The benefit of reduced end point costs achieved by 14
consolidating fewer focal areas in the overlapping portions of many species’ ranges exceeds 15
increased unit costs. The high portion of habitats shared between the species group solutions 16
and the all species solution (Table 2), and among the species group solutions (Figure 5), points 17
to high quality habitat patches that are shared in common by many species. These areas may 18
serve as appropriate core areas for any fish conservation strategies that grow from this effort. 19
By structuring protection efforts around core areas and adding habitats beneficial to individual 20
species groups of interest (Figure 5, Appendix 2), managers can identify fish conservation 21
opportunities that leverage their current organizational capacity, and identify geographic areas 22
where more focus is needed. 23
24
The distribution of focal areas selected in all of our scenarios is widely distributed through the 25
state, and will require a spatially distributed conservation effort to secure against future 26
degradation. The Upper Peninsula has many focal areas (Figure 4) and relatively low human 27
disturbance levels, and thus offers many opportunities to protect species with distributions in the 28
northern part of the state. Further, a large portion of the rivers in the Upper Peninsula runs 29
through lands already managed for conservation (Figure 6a), suggesting that an aquatic 30
conservation strategy for the Upper Peninsula is logically centered on existing conservation 31
lands management efforts. In contrast, the Lower Peninsula, with its higher population 32
densities, and fewer large protected areas will demand conservation strategies that can be 33
implemented within a milieu of widespread private land ownership. Herbert et al. (2010) 34
observed that Michigan’s protected area network is inadequate for representing rare aquatic 35
species. Given this, our focal area networks, particularly for SGCN species, may serve a dual 36
purpose of indicating areas that need conservation, and areas that are candidates for new 37
protected lands. In both the Upper and Lower Peninsulas, very few focal areas have upstream 38
watersheds that are secured within protected areas (Figure 6b). Low levels of watershed 39
protection make watershed management a particularly critical activity for all focal areas due to 40
strong upstream-downstream linkages. 41
42
Systematic planning for protection of riverine features differs from planning efforts in marine or 43
terrestrial settings (Abell et al. 2007). Riverine planning requires a different consideration of 44
longitudinal and lateral connectivity between upstream and downstream portions of dendritic 45
river networks comprised of essentially linear spatial units. Longitudinal connectivity must be 46
considered both from a perspective of interconnections among planning units and in the 47
representation of human disturbance to habitats that may be influenced by spatially distant 48
portions of the river basin (Lake 1980, Pringle 1997, Moyle and Randall 1998). Longitudinal 49
connectivity has been addressed effectively in the literature by defining connectivity among 50
planning units according to the flow path of water between adjacent sub-catchments (Moilanen 51
10
et al. 2008, Klein et al. 2009, Hermoso et al. 2011, Nel et al. 2011, Rivers-Moore et al. 2011). 1
Our study defined connectivity according to flow path of water by using a topologically defined 2
stream network. Human disturbance to habitats has been addressed in other studies by 3
documenting the presence or absence of human activity in sub-basin planning units (Thieme et 4
al. 2007), estimating habitat integrity designations through expert workshops (Nel et al., 2007), 5
considering single threats (e.g. water availability; Roux et al. (2008), or by using estimates of 6
cumulative stress from human activities integrated over the upstream catchment (Linke et al. 7
2007, Esselman and Allan 2011). By using the human disturbance index of Wang et al. (2008), 8
we adopted a representation of disturbance that realistically tracked the relative influences of 9
human activities through the river network, and also considered multiple scales of human 10
influence on watersheds. 11
12
While we drew on best practices from the literature, our study also is unique in several ways. 13
Our use of a sliding scale for representation based habitat occupancy rank represents a 14
departure from simple representation rules based on a flat percentage of range. While both are 15
somewhat arbitrary, the sliding scale leads to a greater emphasis on rare and narrowly 16
distributed species in the planning solution. Second, we ran an unusually high number of 17
iterations for each repeat run of the simulating annealing algorithm. As observed by Ball (2000), 18
the more iterations in a Marxan run, “…the more likely it will be that a better answer (lower 19
objective function value) will arise”. Our specific planning problem had nearly 35 billion 20
(187,0002) different possible configurations of focal areas to explore. Given the large size of the 21
optimization problem and after preliminary study, we found that 1010 iterations per run led to 22
stable solutions across runs. For this reason, we are confident that our solutions are very close 23
to the optimal solution (the lowest possible cost) given the settings we used. 24
25
Our study is also unique in that we attempted to address an apparent paradox in the systematic 26
planning literature for protection of riverine features. Rivers are dendritic features on the 27
landscape that are comprised of connected linear habitat units. However, most previous works 28
that used systematic planning algorithms in riverine applications used terrestrial planning 29
units—proximate (“local”) catchment polygons—rather than river lines as their planning units 30
(e.g., (Hermoso et al. 2011, Nel et al. 2011, Rivers-Moore et al. 2011). Local catchments are 31
convenient because they influence the river reaches embedded in them, they share the network 32
topology of the river, and river protection is often accomplished through land acquisition and 33
management (REFS). However, watershed effect on aquatic species via runoff and 34
groundwater inputs, topography, and land use/cover is only part of the equation. Aquatic 35
organisms are also directly affected by river network setting, connectivity, physicochemical and 36
biological habitats contained within the river channel itself. Here, we used riverine planning 37
units and encountered several challenges in the process. With the identification of focal areas 38
of river channels, managers can implement conservation actions on all locations that have 39
influences on the focal area, including their associated local watersheds. 40
41
The shift from terrestrial to riverine planning units changed the formulation of connectivity in the 42
Marxan cost function and presented conceptual challenges. When catchment planning units 43
are used, they are aggregated based on the weighted sum of their outer boundary length 44
(“boundary cost”). This affects the compactness of the reserve, and is also used to reduce the 45
edge-to-area ratio (Ardron et al. 2010). Edge-to-area ratio is an indicator of the potential for 46
detrimental effects on species at the edge of a habitat patch from contact with humans, edge 47
species, or other threats (Woodroffe and Ginsberg 1998, Kellner et al. 2007). In a riverine 48
environment, compactness is also important for conservation of fishes to provide species with 49
larger habitat patches to carry out their life-histories and to concentrate areas with the best 50
habitats for efficient management. But due to the downstream movement of water, threats don’t 51
11
stop at the edge, but are propagated through the river channel negating some of the benefits a 1
high edge-to-interior ratio. In using river lines as planning units, boundary cost was changed to 2
end point cost—a penalty for the number of tributary junction or network end points. Our 3
implementation still facilitates compactness, because lower end point-to-length ratio will result in 4
a lower cost, but it recognizes stream endpoints as entryways for stressors. By adopting 5
riverine planning units, not only were we able to maintain important aspects of the planning 6
process (e.g., compactness), but we were also able to work with habitat parameters (e.g., end 7
points, river length) that are directly relevant to fishes. Given topological linkages between river 8
segments and their local catchments in GIS databases, post hoc specification of local 9
catchment that are candidate areas for protection is easy to accomplish. 10
11
Identification of freshwater focal areas is but one step toward conserving sufficient high quality 12
habitat for the long-term persistence of healthy fish populations. By their nature, fishes are 13
mobile animals that can move long distances in the channel to seek appropriate habitats for 14
their specific life history needs (spawning, rearing, overwintering, etc.). For this reason and 15
because of the nature of riverine connectivity, “placed-based” conservation planning for fishes 16
requires additional steps beyond the identification of focal areas. Abell et al. (2007) specified 17
two additional management units that can be associated with focal areas to boost the chances 18
for species persistence in a river network. Critical management zones are places whose 19
management is essential to maintaining the functionality of the feature of interest in the focal 20
area. Critical management zones for fishes might include important spawning locations such as 21
riparian wetlands, migration corridors, groundwater recharge zones, and other factors crucial to 22
the maintenance of healthy populations. Catchment management zones are the entire 23
catchment upstream of each focal area (Abell et al. 2007). The intent of catchment 24
management zones is to protect whole watershed processes or patterns that are important to 25
persistence of fishes within focal areas. Catchment management zones constitute places 26
where basic catchment management principles should be applied (e.g., sediment control 27
measures, restricted use of fertilizers and pesticides, etc.) (Naiman et al. 1998). The focal 28
areas defined in this study can serve as a starting point to design a more comprehensive plan 29
for fish conservation in Michigan that includes associated management zones (e.g., Esselman 30
and Allan 2011). 31
32
The populations status of many fish species in the United States is in decline (Jelks et al. 2008), 33
and spatial planning is one step in a more extensive planning process that must ultimately 34
manage the root causes of species decline. Statewide planning efforts such as ours are 35
important because they take place at scales that are relevant to state and federal action to 36
protect species and habitats. The results of the systematic planning exercise presented here 37
point to places where Michigan’s rich fish fauna has a high chance of persistence. To the extent 38
allowed by the data and planning techniques available to us we attempted to conduct our study 39
from a riverine ecological perspective. As such this represents another example in a growing 40
body of literature on systematic freshwater conservation planning. Essential challenges remain 41
(Nel et al. 2009), including a deeper exploration of how linear planning units can be incorporated 42
into planning, future refinement of outputs to include improved models of species distributions 43
and stressors, explicit consideration of range shifts due to climate change, consideration of focal 44
areas to protect spatial metapopulations, and streamlined inclusion of secondary management 45
zones. Our maps of fish conservation focal areas for Michigan can serve to bolster statewide 46
conservation efforts using best available data into regional efforts to protect the critical aquatic 47
resource. 48
49
Acknowledgements 50
51
12
We thank Kevin Wehrly, Jim Breck, the other staff at the Institute for Fisheries Research. 1
Tammy Newcomb played instrumental role in guiding the development of the statewide 2
databases and conservation tools. Two anonymous reviewers provided helpful comments that 3
improved the manuscript. This work was supported by the State Wildlife Action Plan contracted 4
through the Fisheries Division of Michigan Department of Natural Resources. 5
6
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15
Table 1. Summary of results from the best solution for each of the four scenarios. Total length 1
is the sum of all focal areas selected in the best run for each scenario. Percent of available is 2
the portion of available river habitat made up by focal areas. 3
Scenario Length (km)
Prop total (%)
# FAs
Mean FA size (km +/- SE)
Order (+/- SE)
Already protecte
d (%)
Watershed protected
(%)
Large 15,913 17.7 1,07
0 14.87 (+/- 1.22) 2.56 (0.04) 43.3 65.9
Small 14,220 15.8 1,13
2 12.56 (+/- 1.15) 2.44 (0.04) 37.5 70.7
SGCN 13,260 14.8 1,24
2 10.68 (+/- 0.73) 2.61 (0.04) 36.7 71.3
All 18,404 20.5 1,47
0 12.52 (+/- 0.95) 2.44 (0.03) 38.0 70.4
4
Formatted Table
16
Table 2. The proportion of focal area length in the scenario in the first column shared with each 1
of the other scenarios. 2
Scenario Large-bodied Small-bodied SGCN All Large-bodied 1.00 0.49 0.41 0.74 Small-bodied 0.55 1.00 0.52 0.73 SGCN 0.49 0.56 1.00 0.77 All 0.64 0.57 0.56 1.00
3
4
17
1 2
Figure 1. Fish richness predicted from range models of Steen et al. (2008) shown on the 3
1:24,000 streamlines for Michigan 4
18
1 2
3
Figure 2. Representation targets for species (Appendix 1) were fixed to range from 0 to 40% 4
such that they were inversely related to the percentage of available habitat occupied. Species 5
are arranged on the x-axis according to rank order (lower rank = higher occupancy).6
19
1,000,000,000
10,000,000100,000
10,000
5,000
1,000
500
100
10.1
0.001
0.00001
0
0
5000
10000
15000
20000
25000
30000
35000
40000
0 0.5 1 1.5 2 2.5 3 3.5 4
Total length selected (km)
end
po
ints
(#)
Figure 3. Total end points versus total length of focal areas for Marxan solutions at end point modifiers of 10-4 to 109. An end point modifier of 500 was used because it yielded a low number of end points and a low total length.
20
Figure 4. Marxan best (i.e., least cost) solutions for (a) large-bodied fishes, (b) small-bodied fishes and (c) species of greatest conservation need, (d) all species combined.
21
Figure 5. Best results for large-bodied, small-bodied, and species of greatest conservation need groups shown in the same map. Navy blue streams were selected for each of the three species groups.
22
Figure 6. Reaches selected in the all fishes scenario that occur in currently protected (black) and unprotected (gray) catchments. Catchments with more than 90% of their area in lands protected for conservation were considered as “protected” for this purpose.
23
Appendix 1. Fish list including common name, scientific name, size group, predicted range size, occupancy rank, representation target (% and km). Common name Scientific name Range
size (km) Occ. Rnk
Rep. goal (% of range)
Rep. target (km)
All species (km (%))
Redear Sunfish Lepomis microlophus 45,735 1 1.41 646 7,529 (1,165) Least Darter Etheostoma microperca 43,724 2 3.24 1,417 7,867 (555) Black Bullhead Ameiurus melas 42,745 3 4.13 1,765 7,136 (404) Spotted Sucker Minytrema melanops 42,064 4 4.75 1,997 7,563 (379) Green Sunfish Lepomis cyanellus 42,026 5 4.78 2,010 8,034 (400) Orangespotted Sunfish Lepomis humilis 41,921 6 4.88 2,044 6,351 (311) Johnny Darter Etheostoma nigrum 41,641 7 5.13 2,137 8,993 (421) Bluegill Lepomis macrochirus 41,174 8 5.56 2,287 7,853 (343) Bowfin Amia calva 40,793 9 5.90 2,407 11,370 (472) Brassy Minnow Hybognathus hankinsoni 40,793 10 5.90 2,407 11,370 (472) Lake Chubsucker Erimyzon sucetta 39,944 11 6.67 2,665 7,201 (270) Iowa Darter Etheostoma exile 39,670 12 6.92 2,745 8,812 (321) Golden Shiner Notemigonus crysoleucas 39,626 13 6.96 2,758 5,902 (214) Central Stoneroller Campostoma anomalum 38,379 14 8.09 3,106 6,112 (197) Striped Shiner Luxilus chrysocephalus 37,726 15 8.69 3,277 5,938 (181) Common Shiner Luxilus cornutus 37,555 16 8.84 3,320 8,731 (263) Fathead Minnow Pimephales promelas 37,522 17 8.87 3,329 5,251 (158) Northern Redbelly Dace Phoxinus eos 36,573 18 9.73 3,560 7,683 (216) Bluntnose Minnow Pimephales notatus 36,544 19 9.76 3,566 7,763 (218) Redside Dace Clinostomus elongatus 36,081 20 10.18 3,673 7,965 (217) White Crappie Pomoxis annularis 36,028 21 10.23 3,685 5,604 (152) Pumpkinseed Lepomis gibbosus 35,088 22 11.08 3,888 7,418 (191) Warmouth Lepomis gulosus 33,656 23 12.38 4,167 6,838 (164) Brown Bullhead Ameiurus nebulosus 33,649 24 12.39 4,168 6,723 (161) Finescale Dace Phoxinus neogaeus 33,423 25 12.59 4,209 6,256 (149) Brook Trout Salvelinus fontinalis 33,415 26 12.60 4,210 5,561 (132) Rainbow Trout Oncorhynchus mykiss 32,389 27 13.53 4,383 5,970 (136) Redfin Shiner Lythrurus umbratilis 30,931 28 14.86 4,595 4,611 (100) Horneyhead Chub Nocomis biguttata 30,871 29 14.91 4,603 8,321 (181) Blackside Darter Percina maculata 30,094 30 15.62 4,699 8,114 (173) Mimic Shiner Notropis volucellus 29,520 31 16.14 4,764 6,450 (135)
24
Slimy Sculpin Cottus cognatus 28,470 32 17.09 4,866 6,209 (128) White Perch Morone americana 28,115 33 17.41 4,896 4,952 (101) Brook Silverside Labidesthes sicculus 27,372 34 18.09 4,951 6,075 (123) Oriental Weatherfish Misgurnus anguillicaudatus 27,308 35 18.15 4,955 5,294 (107) Mottled Sculpin Cottus bairdii 27,075 36 18.36 4,970 6,578 (132) White Sucker Catostomus commersonii 25,199 37 20.06 5,055 8,199 (162) Rainbow Darter Etheostoma caeruleum 21,496 38 23.42 5,035 6,204 (123) Rockbass Ambloplites rupestris 21,261 39 23.64 5,025 7,418 (148) Quillback Carpiodes cyprinus 20,933 40 23.93 5,010 5,023 (100) Blackstriped Topminnow Fundulus notatus 20,929 41 23.94 5,010 5,072 (101) Lake Chub Couesius plumbeus 19,854 42 24.91 4,946 5,582 (113) Pirate Perch Aphredoderus sayanus 18,937 43 25.75 4,876 4,910 (101) White Bass Roccus chrysops 18,673 44 25.99 4,852 5,031 (104) Yellow Bullhead Ameiurus natalis 18,460 45 26.18 4,833 5,282 (109) Longnose Dace Rhinichthys cataractae 18,106 46 26.50 4,798 5,763 (120) River Redhorse Moxostoma carinatum 17,676 47 26.89 4,753 4,811 (101) Greenside Darter Etheostoma blennioides 17,363 48 27.18 4,718 4,752 (101) Walleye Sander vitreus 17,062 49 27.45 4,683 4,705 (100) Longnose Sucker Catostomus catostomus 16,716 50 27.76 4,641 4,669 (101) Largemouth Bass Micropterus salmoides 15,667 51 28.72 4,499 4,988 (111) Grass Pickerel Esox americanus 15,584 52 28.79 4,487 4,520 (101) Chinook Salmon Oncorhynchus tshawytscha 15,417 53 28.94 4,462 4,524 (101) Northern Pike Esox lucius Linnaeus 14,927 54 29.39 4,387 5,504 (125) Chestnut Lamprey Ichthyomyzon castaneus 14,625 55 29.66 4,338 4,986 (115) Yellow Perch Perca flavescens 14,589 56 29.69 4,332 6,004 (139) Northern Hog Sucker Hypentelium nigricans 14,517 57 29.76 4,320 5,458 (126) Tadpole Madtom Noturus gyrinus 13,696 58 30.51 4,178 4,230 (101) Log Perch Percina caprodes 13,360 59 30.81 4,116 5,217 (127) Black Crappie Pomoxis nigromaculatus 13,245 60 30.92 4,095 4,107 (100) Roseyface Shiner Notropis rubellus 10,352 61 33.54 3,472 3,921 (113) Longnose Gar Lepisosteus osseus 10,275 62 33.61 3,454 3,492 (101) Coho Salmon Oncorhynchus kisutch 9,951 63 33.91 3,374 3,415 (101) Shorthead Redhorse Moxostoma macrolepidotum 8,533 64 35.19 3,003 3,490 (116) Smallmouth Bass Micropterus dolomieu 8,528 65 35.20 3,002 4,205 (140)
25
Emerald Shiner Notropis atherinoides 8,141 66 35.55 2,894 3,144 (109) Silver Lamprey Ichthyomyzon unicuspis 7,736 67 35.92 2,778 3,220 (116) Stonecat Noturus flavus 6,785 68 36.78 2,496 2,811 (113) Silver Redhorse Moxostoma anisurum 6,750 69 36.81 2,485 2,744 (110) Sand Shiner Notropis stramineus 6,134 70 37.37 2,293 2,506 (109) Spotfin Shiner Cyprinella spiloptera 5,840 71 37.64 2,198 2,995 (136) Golden Redhorse Moxostoma erythrurum 5,303 72 38.13 2,022 2,615 (129) River Chub Nocomis micropogon 4,486 73 38.87 1,744 2,067 (119) Greater Redhorse Moxostoma valenciennesi 3,879 74 39.42 1,529 2,107 (138) Black Redhorse Moxostoma duquesnei 3,240 75 40.00 1,296 1,430 (110) Channel Catfish Ictalurus punctatus 89,088 N/A N/A N/A N/A Blacknose Dace Rhinichthys atratulus 66,952 N/A N/A N/A N/A Central Mudminnow Umbra limi 53,420 N/A N/A N/A N/A Brook Stickleback Culaea inconstans 53,290 N/A N/A N/A N/A Creek Chub Semotilus atromaculatus 53,005 N/A N/A N/A N/A Muskellunge Esox m masquinongy 52,573 N/A N/A N/A N/A Brown Trout Salmo trutta 47,292 N/A N/A N/A N/A Sea Lamprey Petromyzon marinus 39,578 N/A N/A N/A N/A Common Carp Cyprinus carpio 24,511 N/A N/A N/A N/A
26
Appendix 2. Overlap of species group focal areas with all species focal areas.
