ATTACHMENT A
Michigan Department of Natural Resources
Study 236104
New Study 2009-2010
Name of Study: Developing decision tools for inland lake management through field sampling and
statistical models linking lakes to landscape context.
A. Problem: Michigan’s abundance of aquatic natural resources poses Fisheries Division with an
impressive multi-system management challenge. How can the state’s diversity of waters be
managed in a sustainable fashion when there are so many of them, and when in-system data are
lacking for so many of them? In particular, Fisheries Division's ability to manage lakes
throughout the state of Michigan is hindered by the sheer number of lakes. Michigan has
approximately 31,000 inland lakes > 0.04 ha. Of these lakes, approximately 2,100 are > 20 ha (J.
Breck, pers. comm.). The collective surface area of Michigan’s inland lakes (~1,400 square
miles) sometimes pales in comparison to that of the Great Lakes (~39,000 square miles).
However, the collective length of shoreline across Michigan’s inland lakes (~17,270 miles)
greatly exceeds that of the Great Lakes (~3,250 miles), and underscores the importance of
understanding the relationship between inland lakes and their surrounding landscape context.
Michigan’s inland lakes support a variety of recreational uses. In particular, Michigan’s inland
waters (lakes and streams) support ~1.3 million anglers per year, representing almost 20 million
angler days of effort on an annual basis (U.S. Department of Interior, Fish and Wildlife Service
and U.S. Department of Commerce, Bureau of Census 1997). In addition, lakes are subject to a
variety of stressors, many of which directly or indirectly affect fish and their habitat as they
involve development within a lake’s riparian/shoreline area or alteration of land-use/cover
throughout a lake’s watershed.
Traditionally, management of Michigan lakes has been conducted primarily on a lake-by-lake
basis. An individual lake has been chosen for sampling, and management decisions for that lake
have been based on sampling in that lake alone. This approach has two disadvantages. First,
little if any information is acquired or applied to management of the many lakes which are not
sampled. To improve this situation, managers need the ability to extrapolate information gained
from sampled to unsampled lakes. As a second disadvantage of the lake-by-lake approach, little
information is gained regarding the factors most important in determining variability among lakes
in sportfish production, water quality, native species biodiversity, etc. As a result, the
opportunity to improve our mechanistic understanding and management of lakes through
comparison of lakes along environmental gradients is foregone. Because lakes across the state
differ greatly in their natural features such as morphometry, climate, and geology, extrapolation
of information from sampled lakes should be based on an understanding of these natural
differences among lakes. This statewide understanding of natural differences among lakes can
also serve as the first step towards developing statewide decision rules for lakes. For example, a
variety of human stressors (such as changes in land use/cover, shoreline modification, aquatic
plant removal, and arrival of invasive species) occur in and around lakes, in various combinations
and intensities. Presently, managers lack quantitative understanding regarding how these stressors
affect aquatic resources.
Complicating the issue is the fact that the response of lakes to the same stressor may differ due to
natural lake differences. Therefore, efforts to quantify the effects of human stressors on lake
resources should occur within a broader framework that controls for or considers the influence of
natural features.
Study 236104 - 2
Why Landscape Limnology and Predictive Classifications are Useful for Multi-Lake
Management. As the groundwork for developing decision tools that aid in multi-lake
management, we must determine the extent to which natural landscape features can predict inlake ones. Here one commonly thinks of in-lake features such as water quality, fish population
demographics, or biotic species composition. In addition, we can consider an in-lake feature to
be a key functional relationship between two in-lake measures, such as the response of
chlorophyll to increasing nutrient levels (Rechow et al. 2005, Soranno et al. in review).
Developing an understanding for how natural landscape features, at multiple spatial scales,
influence key in-lake features is a component of landscape limnology. This relatively new field
of study, inspired by landscape ecology, is the spatially-explicit study of freshwater ecosystems
as they interact with the terrestrial, freshwater, and human components of landscapes to
determine the effects of pattern on ecosystem processes across spatial scales (Soranno et al. 2009,
in review). Once this understanding of lake-landscape relationships is established, work can
proceed that controls for differences in natural landscape features, and investigates topics such as
complex foodweb interactions (and how they are mediated by a lake’s physical setting), or the
response of lakes to management or human stressors (and how variation among lake responses is
mediated by their physical setting).
Improved understanding of how different types of lakes respond to management actions and
human stressors is greatly needed. For example, currently, managers lack the ability to comment
effectively on many permitting and development issues because they lack quantitative
understanding of how these stressors affect aquatic resources. Managers are also limited in their
ability to identify lakes most likely to be invaded by noxious exotic species, or to be particularly
sensitive to negative effects of invasive species. Classification systems, in particular, that are a
product of landscape limnology research, and that group lakes that are naturally similar, can be
used to link statewide monitoring efforts, such as the Status and Trends Program, intensive lake
research studies, and statistical modeling efforts to better address questions such as:
 Has the size structure of fish populations changed as a result of fishing regulation
changes?
 What are the characteristics of lakes that support the state's best fisheries?
 Are stocking efforts working? More specifically, does survival of stocked fish vary
predictably among systems?
 What are the effects of changes in land use practices, plant management, shoreline
development, climate change, or invasive species on the state's aquatic resources?
Toward the goal of integrating statewide monitoring programs with mechanistic research studies,
we (myself, lab personnel, and collaborators) are working to better understand the relationships
between landscape features of lakes and in-lake “response parameters” such as water quality,
plant spatial distribution, fish growth rates, species composition, and recruitment processes, and
also food web energy flowpaths (Wagner et al. 2006, Cheruvelil et al. 2008, Soranno et al. 2009,
in review). Using a variety of statistical approaches, we are quantifying the extent to which
features of lake watersheds at several spatial scales explain variability among lakes in their inlake features, and we are using this analysis to develop predictive classifications for lakes.
‘Predictive’ classifications are those that use one or more predictor variables to classify
ecosystems based on causal relationships with the variable of interest (Brinkhurst 1974).
Predictive classifications are in contrast to other traditional approaches to classification in which
the variable of management interest is the one that classifies the ecosystems. In non-predictive
approaches, in-lake data (i.e., the variable(s) of management interest) are required for a lake to be
classified, so such classifications are not useful for multi-lake management where in-lake data
often are lacking. Non-predictive classifications also have the drawback that they are not based
on the underlying relationships linking lake landscapes to in-lake features.
Predictive
Study 236104 - 3
classifications do not suffer from these limitations, in part because landscape data (obtained from
GIS databases) are available for virtually all lakes. Therefore, we suggest that predictive
classifications have been understudied by ecologists and underused by ecosystem managers.
Summary of Selected Fish-Related Classification Efforts. In the past, classifications have been
developed to group lakes according to a variety of lake response variables (see Leach and Herron
1992 for a useful summary). Among lake classifications that have sought to predict
characteristics of lake fish assemblages, fish species composition has been the most prominent
response variable (see Johnson et al. 1977, Schneider 1981, Schupp 1992 for examples).
Magnuson et al. (1998) in particular provided a very useful conceptual framework in which fish
species richness is viewed as the outcome of immigration and extinction events in lakes, building
from previously established Island Biogeography Theory (MacArthur and Wilson 1967). For
example, past and present surface water connections between lakes represent pathways for
immigration events of new species reaching a lake, whereas lake size (representing the severity of
ecological conditions within the lake) is associated with the rate at which existing species become
extirpated in a lake over time.
Investigations of factors influencing fish species composition can aid in decisions regarding
where to stock fish, where to focus efforts to protect unique or threatened species, and where to
encourage angler effort for particular species. However, information on fish species composition
alone is of limited value to managers when facing decisions related to setting fishing regulations,
assessing status of a fishery, and protecting vital habitat. In particular, differences among lakes in
production of a particular species can result in the need for substantially different regulations
among lakes. Accordingly, classification tools have been developed to predict fish standing crop
or potential harvest (Ryder 1965, Hanson and Leggett 1982, Jones and Hoyer 1982, Ryder 1982,
Yurk and Ney 1989, Downing et al. 1990). More recently, classifications have been developed to
predict key life history parameters, and hence safe exploitation levels, for particular species
(Shuter et al. 1998).
As stated previously, many classifications to date have required in-lake data to classify lakes, thus
limiting their application to relatively few lakes for which data are available. Classifications
using only landscape-based data (available through GIS) have much broader potential for
application. However, most classifications to date have considered a limited number of landscape
features. For example, individual studies have focused on broad regional features such as
ecoregion (Omernik and Griffith 1991), other studies have demonstrated the importance of subregional features such as hydrological flow and connectivity to in-lake features (Kratz et al. 1997,
Hershey et al. 1999, Soranno et al. 1999, Riera et al. 2000), while still other studies have
demonstrated that local features of lakes, such as size and depth, provide some predictive ability
for in-lake features (Eadie and Keast 1984, Dillon and Molot 1997, Nate et al. 2000). However,
few if any studies have included all pertinent spatial scales of landscape context. Therefore,
comparison of the relative ability of each spatial scale to predict in-lake features has not been
conducted, nor has such a comparison been done for a variety of in-lake features. For example,
some in-lake features may respond to local landscape features, whereas others may be more
driven by sub-regional or regional factors. Establishing this understanding will help managers
identify features of lake landscapes that are most influential in determining in-lake features.
Finally, while there is a rich history of intensive research on individual lakes, giving critical
insights into the processes structuring lake ecosystems and fish assemblages, there has been
surprisingly little systematic attention paid to determining the extent to which such studies can be
reliably extrapolated to other lakes. My research program also conducts intensive in-lake
investigations aimed at learning more about the processes through which human stressors affect
lakes, with an emphasis on defining reliable bounds for extrapolation. In the next 5 years, I plan
to continue to address the question of reliable extrapolation of intensive field research.
Study 236104 - 4
B. Objective: Develop a research, education, and outreach/service program aimed at improving
inland lake management with a view toward an enhanced understanding of how natural landscape
features of lakes influence food web interactions and the response of aquatic resources to human
stressors.
C. Expected Results and Benefits: In my research program, quantifying lake-landscape
relationships serves as the foundation for further investigations of the effects of stressors on lake
fish assemblages and water quality. Many practical outcomes of this work are possible. For
example, predictive classifications are used for developing monitoring programs. By reducing
variation among lakes within a strata, the program has heightened sensitivity to detect trends over
time. Classifications can also be quite useful in determining reference conditions for systems
(Reynoldson et al. 1997) and I am currently collaborating with individuals on this topic as well.
Classifications and related landscape limnology research that investigates relationships between
natural landscape features and in-lake features are vitally necessary for subsequent work that
investigates the effects of human stressors on lakes. In addition, this knowledge provides useful
guidance for reliably extrapolating information from sampled or intensively studied lakes to those
lacking data. Finally, the landscape limnology and predictive classification approach is a very
useful framework in which I conduct more intensive field observations. Many questions
regarding the effects of human stressors on lakes can not be addressed solely by use of extensive
monitoring data bases. Rather, intensive sampling is needed, especially if the emphasis is on
isolating mechanisms responsible for observed patterns. In that vein, my laboratory is conducting
two types of field projects (both of which are/have been funded elsewhere). First, we are
continuing to investigate the effects of residential lakeshore development and invasive species
(Zebra mussels, Dreissene polymorpha) on benthic-pelagic food web linkages in lakes. Second,
we continue to investigate the effects of spring angling and residential lakeshore development on
the reproductive dynamics and population demographics of bass populations.
D. Procedure:
Job 1. Review literature and unpublished studies on inland lake ecosystems, lake
classification, multi-lake analysis of fish population dynamics and management, lake
food web interactions, black bass reproductive ecology and population demographics,
and benthic-pelagic linkages in lakes. Synthesize literature as needed using informal
and formal (e.g., meta-analysis) methods.
Job 2. Expand the PERM research program into new areas complementary to the existing
funded research programs with an emphasis on effects of landscape context on fish
recruitment and food web interactions, lake classification and multi-lake
management, effects of human stressors on fish assemblages, and effects of angling
and habitat alteration on the reproductive ecology of nesting centrarchids. Seek
additional funding from a variety of partners to enhance the research program.
Job 3. Provide service to Michigan DNR Fisheries Division, other partners of the PERM
program, including Michigan State University, and the broader scientific community
with activities such as discussion facilitation, program reviews, review of journal
articles and grant proposals, statistical consulting, membership on committees,
editorial activities, academic governance, and participation in activities of
professional societies.
Job 4. Provide training and other educational opportunities for staff or students from
Michigan DNR Fisheries Division, other partners of the PERM program including
Michigan State University, and the broader scientific community in a variety of areas
related to inland lake ecology and management in the form of formal courses, shortcourses, workshops, seminars, and training programs. Such activities should have
pre-service, in-service, and public outreach components.
Study 236104 - 5
Job 5. Supervise graduate research assistants, post-doctoral associates and research aides
whose work is supported by funds obtained from sources other than Michigan
Department of Natural Resources.
Job 6. Prepare annual report and as appropriate communicate program results in the form of
peer-reviewed publications, reports, popular articles, and presentations.
Job 7. Write manuscripts for publication. A fisheries research report or journal publication
will be prepared describing findings of the evaluation.
Job 8. Publish manuscript. This job entails final editing and printing of the research
manuscript or journal articles produced under job 7.
Job 9. Write final report. A final report citing the publications produced under job 8 will be
prepared.
E.
Schedule:
Proposed work
2009-10 2010-11 2011-12 2012-13 2013-14
Job 1 Review literature
X
X
X
X
X
Job 2 Expand research program
X
X
X
X
X
Job 3 Provide professional service
X
X
X
X
X
Job 4 Provide training
X
X
X
X
X
Job 5 Mentor students and research aides
X
X
X
X
X
Job 6 Write annual performance report
X
X
X
X
X
Job 7 Write manuscript
X
X
X
X
X
Job 8 Publish manuscript
X
X
X
X
X
Job 9 Write final report
X
F. Geographic Location: The scope of this research is state-wide, and as such, covers primarly
inland lakes in the Lake Michigan, Lake Huron, Lake Superior, and Lake Erie watersheds.
G. Personnel: Mary T. Bremigan, Associate Professor, Dept. of Fisheries and Wildlife, Michigan
State University.
H. Products and deliverables for current fiscal year:
Manuscripts/theses/dissertations: Along with collaborators, I plan to submit several
manuscripts, related to the research program described above, for publication in peerreviewed journals. One manuscript will provide an overview of how landscape limnology
research can be used to develop predictive classifications for multi-lake management. Two
manuscripts will investigate relationships between fish assemblages and landscape features.
Included in this effort will be the development of a predictive classification for fish species
richness across 360 lakes in 5 states, based on natural landscape features. In addition, this
effort will use the natural landscape classification to quantify the response of lakes to 9
human stressor predictors. These two manuscripts will constitute the master’s thesis for a coadvised graduate student, Brett Alger. In addition, we will submit a manuscript that
quantifies relationships between residential shoreline development and littoral zone habitat.
Study 236104 - 6
We also will continue to work on manuscripts exploring: (a) benthic-pelagic linkages in lakes
across a gradient of residential development, (b) the potential for fish fin tissue to provide
reliable stable isotope information (as opposed to muscle tissue), and (c) effects of spring
angling on reproductive ecology of black bass.
Research Proposals: Along with collaborators, we will submit a landscape-limnology related
proposal to the National Science Foundation. I will also explore opportunities with my
collaborator, Dr. Kim Scribner, for obtaining additional funding to investigate the effects of
angling on reproductive ecology and population demographics of black bass.
Workshops: During this fiscal year I will consult with Fisheries Division personnel to plan
and prepare for a workshop to be delivered to Fisheries Division biologists in the next fiscal
year. The topic and format will be developed through the consultations. These workshops
are intended to help Division biologists stay up-to-date in terms of their working knowledge
of fisheries management issues and techniques.
Professional service: I will provide an analysis of the Fisheries Division’s Status of the
Fishery reports. I will review a representative sample of recent reports, and provide an
evaluation in which I offer suggestions regarding opportunities to improve these reports
through: (a) using more innovative analyses, (b) integrating the individual lake analyses with
statewide monitoring data, (c) evaluating the rationale for management actions and (d)
investigating the outcome of implemented management actions.
Literature Cited:
Cheruvelil, K.S., P.A. Soranno, M.T. Bremigan, T. Wagner, and S.L. Martin. 2008. Grouping lakes
for water quality assessment and monitoring: the roles of regionalization and spatial scale.
Environmental Management 41:425-440.
Dillon, P.J. and L.A. Molot. 1997. Effect of landscape form on export of dissolved organic carbon,
iron, and phosphorus from forested stream catchments. Water Resources Research 33:25912600.
Downing, J.A., C. Plante, and S. Lalonde. 1990. Fish production correlated with primary
productivity, not the morphoedaphic index. Canadian Journal of Fisheries and Aquatic Sciences
47:1929-1936.
Eadie, J.M. and A. Keast. 1984. Resource heterogeneity and fish species diversity in lakes.
Canadian Journal of Zoology 62:1689-1695.
Hanson, J.M. and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Canadian
Journal of Fisheries and Aquatic Sciences 39:257-263.
Hershey, A.E., G.M. Gettel, M.E. McDonald, M.C. Miller, H. Mooers, W.J. O’Brien, J. Pastor, C.
Richards, and J.A. Schuldt. 1999. A geomorphic-trophic model for landscape control of arctic
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Johnson, M.G., J.H. Leach, C.K. Minns, and C.H. Olver. 1977. Limnological characteristics of
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Study 236104 - 7
Leach, J.H. and R.C. Herron. 1992. A review of lake habitat classification. Pages 27-57 in W.D.N.
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Nate, N.A., M.A. Bozek, M.J. Hansen, and S.W. Hewett. 2000. Variation in walleye abundance with
lake size and recruitment source. North American Journal of Fisheries Management 20:107-114.
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managing water quality. Journal of Soil and Water Conservation September-October: 334-340.
Reckhow, K.H.,G. Arhonditsis, M. Kenney, L. Hauser, J. Tribo, C. Wu, K. Elcock, L.J. Steinberg,
C.A. Stow, and S. McBride. 2005. A predictive approach to nutrient criteria. Environmental
Science & Technology, 39: 2913-2919.
Reynoldson, T.B., R.H. Norris, V.H. Resh, K.E. Day and D.M. Rosenberg. 1997. The reference
condition: a comparison of multimetric and multivariate approaches to assess water-quality
impairment using benthic macroinvertebrates. Journal of the North American Benthological
Society 16:833-852.
Riera, J.L., J.J. Magnuson, T.K. Kratz, and K.E. Webster. 2000. A geomorphic template for the
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Schneider, J.C. 1981. Fish communities in warmwater lakes. Michigan Department of Natural
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communities. Minnesota Department of Natural Resources Investigational Report 417.
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for regional management of fish stocks: the inland lake trout (Salvelinus namaycush) fisheries of
Ontario. Canadian Journal of Fisheries and Aquatic Sciences 55:2161-2177.
Soranno, P.A., K.E. Webster, J.L. Riera, T.K. Kratz, J.S. Baron, P. Bukaveckas, G.W. Kling, D.
White, N. Caine, R.C. Lathrop, and P.R. Leavitt. 1999. Spatial variation among lakes within
landscapes: ecological organization along lake chains. Ecosystems 2:395-410.
Soranno, P.A., K.E. Webster, K.S. Cheruvelil, and M.T. Bremigan. 2009. The lake landscape-context
framework: linking aquatic connections, terrestrial features and human effects at multiple spatial
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Soranno, P.A., K.E. Webster, K.S. Cheruvelil, and M.T. Bremigan, T. Wagner, and C.A. Atow. In
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