Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin

Abstract

To identify past successes and future opportunities for improved fisheries management in Wisconsin, we synthesized size-structure information on 19 gamefish species from 1944 to 2012, incorporating data on more than 2 million measured individuals. Since the 1940s, mean and mean maximum sizes of five “gamefish” species (Lake Sturgeon Acipenser fulvescens, Largemouth Bass Micropterus salmoides, Smallmouth Bass M. dolomieu, Northern Pike Esox lucius, and Sauger Sander canadensis) have stayed fairly stable, and one (Muskellunge E. masquinongy) initially dropped and then rebounded—most likely as a product of increased catch-and-release fishing and restrictive harvest regulations. In contrast, four “panfish” species (i.e., Bluegill Lepomis macrochirus, Green L. cyanellus, Yellow Perch Perca flavescens, and Black Crappie Pomoxis nigromaculatus), which have not received the same conservation management attention, have experienced substantial and sustained erosions in size over the same period. Regulations for many species and species complexes have been cyclical over time, illustrating the challenge of consistently managing fisheries. Our long-term retrospective analysis was effective at identifying new opportunities for improved fisheries management in Wisconsin (i.e., panfish management). We therefore encourage other big data retrospective approaches within and across regions to identify past successes and future opportunities in other fisheries management programs.

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Seventy-Year Retrospective on Size-Structure
Changes in the Recreational Fisheries of Wisconsin
Andrew L. Rypel, John Lyons, Joanna D. Tober Griffin & Timothy D. Simonson
To cite this article: Andrew L. Rypel, John Lyons, Joanna D. Tober Griffin & Timothy D.
Simonson (2016) Seventy-Year Retrospective on Size-Structure Changes in the Recreational
Fisheries of Wisconsin, Fisheries, 41:5, 230-243, DOI: 10.1080/03632415.2016.1160894
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230 Fisheries | Vol. 41 • No. 5 • May 2016
FEATURE
Seventy-Year
Retrospective on
Size-Structure Changes
in the Recreational
Fisheries of Wisconsin
Andrew L. Rypel
Wisconsin Department of Natural Resources, Bureau of Science Services, 2801 Progress Road, Madison, WI 53716,
and Center For Limnology, University of Wisconsin-Madison, Madison, WI. E-mail: andrew.rypel@wisconsin.gov
or andrewrypel@gmail.com
John Lyons
Wisconsin Department of Natural Resources, Bureau of Science Services, Madison, WI
Joanna D. Tober Grin
Wisconsin Department of Natural Resources, Bureau of Fisheries Management, Madison, WI
Timothy D. Simonson
Wisconsin Department of Natural Resources, Bureau of Fisheries Management, Madison, WI
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Fisheries | www.sheries.org 231
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232 Fisheries | Vol. 41 • No. 5 • May 2016
To identify past successes and future opportunities for improved fisheries management in Wisconsin, we synthesized size-
structure information on 19 gamefish species from 1944 to 2012, incorporating data on more than 2 million measured indi-
viduals. Since the 1940s, mean and mean maximum sizes of five “gamefish” species (Lake Sturgeon Acipenser fulvescens,
Largemouth Bass Micropterus salmoides, Smallmouth Bass M. dolomieu, Northern Pike Esox lucius, and Sauger Sander
canadensis) have stayed fairly stable, and one (Muskellunge E. masquinongy) initially dropped and then rebounded—most
likely as a product of increased catch-and-release fishing and restrictive harvest regulations. In contrast, four “panfish”
species (i.e., Bluegill Lepomis macrochirus, Green L. cyanellus, Yellow Perch Perca flavescens, and Black Crappie Pomoxis
nigromaculatus), which have not received the same conservation management attention, have experienced substantial
and sustained erosions in size over the same period. Regulations for many species and species complexes have been
cyclical over time, illustrating the challenge of consistently managing fisheries. Our long-term retrospective analysis was
eective at identifying new opportunities for improved fisheries management in Wisconsin (i.e., panfish management).
We therefore encourage other big data retrospective approaches within and across regions to identify past successes and
future opportunities in other fisheries management programs.
Retrospectiva de 70 años sobre los cambios en la estructura de tallas en las pesquerías recre-
ativas de Wisconsin
Con el fin de sintetizar los éxitos pasados y las oportunidades futuras para mejorar el manejo pesquero en Wisconsin, en
este trabajo se sintetizó información sobre la estructura de tallas de 19 especies de pesca, de 1944 a 2012, que incorpora
datos de >2 millones de individuos muestreados. Desde la década de 1940, las tallas promedio y máximas promedio de
cinco grandes especies (esturión de río Acipenser fulvescens, lobina negra Micropterus salmoides, lobina boca chica M.
dolomieu, lucio Esox lucius y el sauger Sander canadensis) se han mantenido relativamente estables y en una especie (la
muskallonga E. masquinongy) inicialmente cayeron pero luego se recuperaron, presumiblemente debido a la práctica de
captura-liberación y a restricciones en la captura. En contraste, cuatro especies de peces (i.e., mojarra oreja azul Lepomis
macrochirus, pez sol L. cyanellus, perca amarilla Perca flavescens y la mojarra negra Pomoxis nigromaculatus) que durante
ese mismo periodo no recibieron la misma atención en cuanto a medidas de conservación, han experimentado una reduc-
ción importante y sostenida en la talla. Las regulaciones para muchas especies y complejos de especies han sido cíclicas
en el tiempo, lo cual subraya el reto de contar con un manejo pesquero consistente. Este análisis retrospectivo de largo
plazo sirvió para identificar nuevas oportunidades y mejorar el manejo pesquero en Wisconsin (i.e., peces sarteneros). Se
invita a aplicar enfoques retrospectivos a otros programas de manejo pesquero así como también aplicarlos hacia el inte-
rior y entre regiones con el fin de identificar éxitos pasados y oportunidades para el futuro.
Rétrospective de soixante-dix ans de changements dans la structure des tailles dans la pêche
sportive au Wisconsin
Pour identifier les réussites passées et les possibilités futures d’amélioration de la gestion des pêches dans le Wisconsin,
nous avons synthétisé des informations sur la structure des tailles de 19 espèces de pêche sportive au cours des années
1944 à 2012, intégrant des données de plus de 2 millions d’individus mesurés. Depuis les années 1940, la moyenne et la
moyenne des tailles maximales de cinq espèces « de pêche sportive » (l’esturgeon jaune Acipenser fulvescens, l’achigan à
grande bouche Micropterus salmoides, l’achigan à petite bouche M. dolomieu, le grand brochet Esox lucius et le doré noir
Sander canadensis) sont restés relativement stables, tandis qu’une autre (le maskinongé E. masquinongy) a initialement
diminué, puis rebondi vraisemblablement en raison de l’augmentation de la pêche avec remise à l’eau et des règlements
restrictifs en matière de pêche. En revanche, les quatre espèces de « crapet » (c.-à-d. le crapet arlequin Lepomis macrochi-
rus, le crapet vert L. cyanellus, la perchaude Perca flavescens et la marigane noire Pomoxis nigromaculatus), qui n’ont pas
reçu la même attention en matière de gestion de conservation, ont connu des érosions importantes et durables de taille au
cours de la même période. Les règlements pour de nombreuses espèces et complexes d’espèces ont été cycliques au fil
du temps, illustrant le défi d’une gestion cohérente de la pêche. Notre analyse rétrospective à long terme a été ecace à
identifier de nouvelles opportunités pour l’amélioration de la gestion des pêches dans le Wisconsin (gestion des crapets).
Par conséquent, nous encourageons les autres approches rétrospectives de données importantes dans et entre les régions
afin d’identifier les réussites passées et les possibilités futures dans d’autres programmes de gestion des pêches.
INTRODUCTION
Understanding factors that regulate size structure in
exploited sh populations remains one of the central goals of
sheries science (Haedrich and Barnes 1997; Pauly et al. 1998;
Hilborn et al. 2003). In particular, evaluating effects of sheries
management policies on size structure is crucial to prioritizing
future management activities (Cooke and Schramm 2007;
Isermann 2007; Hobday et al. 2011). Size-structure changes to
sh populations can occur for a variety of reasons, including
interactions between angler effort and harvest preferences (Oh
et al. 2005), regulations and catchability (Askey et al. 2006),
and growth rate change, whether through density dependence
(Walters and Post 1993) or extrinsic factors like climate (Rypel
2009, 2012). Yet most studies focus on either one species in
a single ecosystems responding to a single regulatory change
(Austen and Orth 1988; Newman and Hoff 2000; Paukert et
al. 2002), or regulatory changes on multiple populations of the
same species (Clark et al. 1981; Lyons et al. 1996; Jacobson
2005). Few studies have evaluated long-term trends in the size
structure of numerous managed species across a large region
in response to species-complex shing regulations that change
over time (but see Olson and Cunningham 1989; Grant et al.
2004; Hilborn and Ovando 2014). Long-term data sets are one
tool that might be useful for uncoupling and understanding
drivers of size-structure change in sh populations (Magurran et
al. 2010; Last et al. 2011). Accordingly, long-term and large-
scale databases (and analyses utilizing these digital assets)
carry a high potential for improving and optimizing sheries
management strategies.
In many U.S. states, biologists have now been collecting
data on sh populations for more than 100 years (Nielsen
1999). Because these data are digitized into processesable
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Fisheries | www.sheries.org 233
databases, they represent a new and unprecedented resource for
understanding how natural resource policies have shaped the
quality of sh populations over long time periods. The potential
behind this type of approach initially became apparent through
the study of “shifting baselines;” that is, the gradual decrease
in natural resource expectations over long, often generational,
timescales (Baum and Myers 2004; Sáenz-Arroyo et al. 2005;
Humphries and Winemiller 2009). However, many shifting-
baseline studies have been forced to conduct analyses using
crude forms of data; for example, old photographs (e.g., Figure
1), shing logs, or anecdotal accounts (Baum and Myers 2004;
Winemiller 2005). In contrast, state sheries management
databases, though not encompassing as long a period of change,
contain considerably more detailed and standardized information
on the abundance and size structure of sheries over time.
Thus, once compiled, these databases represent important and
unprecedented resources for documenting long-term change in
sh populations at broad scales (Pinsky et al. 2011; Garibaldi
2012; McManamay and Utz 2014).
In this study, we synthesized a large and continually updated
data set on the size structure of inland Wisconsin gamesh
populations that dates back to the 1940s. In particular, we
focused on evaluating regional trends in size structure over
time for common sh species pursued by Wisconsin anglers.
Our primary goal was to evaluate how a retrospective approach
might be useful in identifying past successes and future
opportunities for improved sheries management. We also
reviewed the history of the state’s shing regulations to provide
a context for how regulatory changes may have contributed to
observed trends in size structure.
METHODS
Data
Relatively consistent sheries surveys of inland lakes and
streams in Wisconsin have been conducted by the Wisconsin
Department of Natural Resources (WDNR) and its predecessor
the Wisconsin Conservation Department for more than 70 years
(Figure 2). Standard fyke net and boat electroshing surveys
tend to dominate the Wisconsin database. A large fraction of
available fyke net data on certain species (e.g., Walleye Sander
vitreus and Muskellunge Esox masquinongy) originates from
annual spring netting surveys following ice-out. These data are
used for abundance estimates, mark-and-recapture surveys for
estimating population sizes, and egg-take procedures for the
hatcheries. Boat-mounted boom and mini-boom electroshing
surveys became increasingly common in the late 1950s and
1960s, reecting Wisconsin’s leadership in the development of
this sampling technology (Deichelbohrer 1961; Novotny and
Priegel 1974). Boat electroshing surveys have typically been
conducted during summer months, although some occur in
Figure 1. Aldo Leopold photographed at his family home as a child with a stringer of panfish (Yellow Perch), 1899. Photo credit: Wisconsin
Historical Society, WHS-93910. Permission to reuse must be obtained from the Wisconsin Historical Society.
Downloaded by [Wisconsin Dept of Natural Resources] at 11:18 26 April 2016

234 Fisheries | Vol. 41 • No. 5 • May 2016
early spring to recapture sh tagged during fyke net surveys for
population estimates. Summer fyke netting surveys have been
collected more sporadically over time and usually in conjunction
with more comprehensive pansh surveys.
Until the 1980s, biological data were stored in paper
format at WDNR eld ofces. Biologists began to transition
to computerized databases during the 1980s and 1990s. In
2001, the WDNR Fisheries Management Database (FMDB)
was created to provide a centralized internet transactional
and warehouse system. Since this time, all new sheries data
collected have been deposited into FMDB. In addition, a process
was initiated to integrate legacy data sets into FMDB over time.
The availability of sheries data by year in the database shows
a strong and positive exponential trend from less than 500,000
records in 1985 to more than 3 million records today. In this
analysis, we focused on 19 sh species routinely collected and
measured by WDNR personnel for inland sheries management.
Though nonscientic, we use the terms “gamesh,” “pansh,”
and “other” as broad species categories encompassing differing
management regimes because these categories are drawn
directly from the Wisconsin shing regulations pamphlet and
are indicative of the level of conservation management awarded
species.
Statistical Analyses
We classied seven shes (Lake Sturgeon Acipenser
fulvescens, Largemouth Bass Micropterus salmoides,
Smallmouth Bass M. dolomieu, Muskellunge, Northern Pike E.
lucius, Walleye, and Sauger S. canadensis) as gamesh species.
The remaining 12 species were classied as either pansh
or “other” species and included Green Lepomis cyanellus,
Pumpkinseed L. gibbosus, Bluegill L. macrochirus, Yellow
Perch Percaavescens, Black Crappie Pomoxis nigromaculatus,
Rock Bass Ambloplites rupestris, Black Bullhead Ameiurus
melas, Yellow Bullhead A. natalis, Brown Bullhead A.
nebulosus, White Sucker Catostomus commersonii, Common
Carp Cyprinus carpio, and White Bass Morone chrysops. For all
focal species, a mean size per year was estimated by calculating
the mean total length for each population during each year and
then calculating a yearly mean (for the state) from the set of
population means (Beard and Kampa 1999). In addition, for
mixed effects model development (see below), a mean size per
gear and per population was also estimated. For mean maximum
length, the largest sh from each population-year was identied
and statewide mean maximum total lengths calculated per year
as above. Trends in mean size and mean maximum size over
Figure 2. Spring fyke net sampling of fish populations in Wisconsin lakes has changed little over time between (A) 1935 and (B) 2013. (C) Fu-
ture U.S. President Dwight Eisenhower and colleagues displaying a large catch of Wisconsin Muskellunge in 1946. (D) Present-day angler re-
leasing an adult Muskellunge captured from a Wisconsin lake. (E) Large harvest of crappie and White Bass from Lake Mendota, Dane County,
Wisconsin, 1956, and (F) authors (ALR and JL) with a recent catch of Wisconsin panfish, 2014. Photo credits: Photo A: Wisconsin Historical
Society, WHS-82746. Permission to reuse must be obtained from the Wisconsin Historical Society. Photo B: from Steve Gilbert; used with per-
mission. Photo C: Wisconsin Historical Society, WHS-2822. Permission to reuse must be obtained from the Wisconsin Historical Society. Photo
E: Wisconsin Historical Society, WHS-92299. Permission to reuse must be obtained from the Wisconsin Historical Society. Photo F: from Greg
Sass; used with permission.
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Fisheries | www.sheries.org 235
time were assessed using mixed effects regression models
(Bolker et al. 2009). For each species mean size model, mean
length per gear in each population was the dependent variable,
year was a predictor (xed effect), and population and gear type
were random variables. For each species mean maximum size
model, the mean maximum length was the dependent variable,
year was again the xed effect predictor, and population was
a random variable. Gear was not used as a random variable in
maximum size models, because for these models we were only
interested in the largest sh captured regardless of gear. And
because only one gear was used per population to sample each
species, the population random effect variable captures this
dynamic. Mixed effects models have been increasingly applied
to large ecological and sheries data sets possessing complex
and uneven data structures (Myers and Worm 2003; Hyun et al.
2014; Daufresne et al. 2015). For example, in this case, there
may be concern that lakes containing larger or smaller sh may
have been sampled earlier in the record or that the gear types
used in sampling have changed over time, thereby creating the
illusion of a body size trend over time when none in fact actually
exists. Mixed effects models deal with these possibilities in
a statistically conservative manner through the construction
of covariance matrices and examination of residuals around
any covariance effect (Bolker et al. 2009). Though inherently
more complex than standard general linear models, mixed
effects models are an effective tool for dealing with covariance
dynamics across large and complex data matrices while still
allowing for maximum data usage (Bolker et al. 2009). As a
precautionary measure, we also reran all mixed effects model
regressions for mean and mean maximum size excluding data
from the 1940s (the fyke net intensive period) to assess whether
the same species would retain signicance. In the case of one
species (Muskellunge), we also present a nonlinear (quadratic)
mixed effects regression because of a visually apparent parabolic
trend in the data. We acknowledge that statistical procedures
exist (e.g., Akaike information criterion) to determine
appropriate curve ts; however, we present this model, in this
one case, without curve-tting statistics for brevity.
To address potential effects of density-dependent trends
over time, we also summarized catch per unit effort (CPUE)
data for a subset of focal species that received more sampling
attention over time. Fisheries sampling protocols have often
been tailored toward these species and thus they represent the
best abundance data available. We summarized CPUE data for
a standard gear type for each species, calculating yearly means
of boat electroshing CPUE for Largemouth and Smallmouth
Bass, Bluegill, and Pumpkinseed and mean fyke net CPUE
for Muskellunge, Northern Pike, Walleye, Black Crappie, and
Yellow Perch. Statewide yearly means in CPUE for each species
were calculated as above for size structure. Again, trends in
CPUE for each species were evaluated using mixed effects
regression models with mean log10(CPUE) as a dependent
variable, year as a xed effect, and population as a random
variable. These analyses were conducted with recognition
that CPUE is not always a strong indicator of sh abundance
because of factors ranging from density-dependent catchability
to seasonality and gear bias (Shardlow et al. 1985; Pope and
Willis 1996; Paukert 2004). Finally, we summarized the general
usage of two primary gear types (fyke nets and electroshing)
by decade for seven of the focal species used in this study (Table
1). These data are presented with the purpose of providing
more detailed information on how usage of the dominant gear
types has changed over time. Statistics for all analyses were
considered signicant at α < 0.05.
Table 1. Percentages of statewide catch by decade for seven primary sportfishes in fyke nets versus electrofishing gears. Early in the
record, collections were more heavily composed of fish sampled through fyke netting. Electrofishing rapidly became popular during the
mid- to late 1950s and percentages by gear have remained approximately steady for most species since that time.
Decade Black Crappie Bluegill Yellow Perch Largemouth Bass Northern Pike Muskellunge Walleye
Fyke nets
1940s 90.8 78.7 72.6 86.5 91.0 100.0 95.3
1950s 58.5 37. 2 45.0 27. 9 59.5 88.5 75.7
1960s 41.7 24.9 17.9 16.9 44.0 67. 4 37. 3
1970s 55.9 30.4 37. 9 20.1 45.0 40.3 51.3
1980s 61.4 30.8 55.0 15.9 58.9 48.9 62.1
1990s 72.2 41.3 58.4 19.6 68.8 50.6 44.2
2000s 57. 5 42.3 55.1 21.1 77.3 60.5 51.7
2010s 78.0 20.0 66.4 17.9 82.9 77.3 48.5
Electrofishing
1940s 0.0 4.9 1.4 2.2 0.0 0.0 0.1
1950s 10.2 1.8 15.0 18.2 2.3 9.2 10.0
1960s 50.8 39.3 73.2 77.0 47. 8 29.8 59.0
1970s 38.3 3 7.3 53.2 74.5 48.3 56.3 45.6
1980s 34.2 43.2 33.7 79.7 39.0 49.4 36.0
1990s 19.2 4 7.2 35.4 78.2 30.7 49.3 55.5
2000s 34.5 54.6 44.7 78.4 22.4 38.9 46.7
2010s 18.9 80.0 33.6 79.3 16.5 22.7 51.4
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236 Fisheries | Vol. 41 • No. 5 • May 2016
Figure 3. Trends in average (blue circles) and maximum (green circles) size of 19 fishes in Wisconsin over the last 70 years. Regression lines
indicate significant (P < 0.05) mixed eects regression models. Model statistics for any species can be viewed in Table 2. Error bars represent
the mean ± 1 SE.
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Fisheries | www.sheries.org 237
RESULTS
We compiled data from 19 species on the measured total
lengths of more than 1.9 million individual sh in Wisconsin
encompassing the time period 1944–2012. Walleye was the
species with the most available data (N = 453,006 individual
sh), followed by Largemouth Bass (N = 276,510) and Northern
Pike (N = 245,577). The most data-poor species evaluated
were White Bass (N = 7,882), Brown Bullhead (N = 8,229),
and Sauger (N = 8,893; Table 2). Mean sample size was
approximately 100,000 individual sh per species.
In total, six of 19 species (32%) showed signicant declines
in mean size over the period of record (Table 2, Figure 3).
Every pansh species but Pumpkinseed showed a signicant
decline over time in mean size. Six of 19 species (32%) also
showed signicant declines in mean maximum size, including
again all pansh species except Pumpkinseed (Table 2). Four
species (Northern Pike, Muskellunge, Largemouth Bass, and
Brown Bullhead) showed signicant increases in mean size,
and three of these species, with the exception of largemouth
bass, also showed signicant increases in mean maximum
size. Nine species (Lake Sturgeon, Smallmouth Bass, Sauger,
Pumpkinseed, Black Bullhead, Yellow Bullhead, Common Carp,
White Sucker, and White Bass) showed no apparent trends in
mean size over the period of record, and most of these same
species showed no apparent trends in mean maximum size
(Table 2). Of the nine species for which adequate CPUE data
were available, three species (Largemouth Bass, Smallmouth
Bass, and Bluegill) showed signicant increases in CPUE, four
species (Muskellunge, Black Crappie, Pumpkinseed, and Yellow
Perch) showed no trend in CPUE, and two species (Walleye and
Northern Pike) showed decreasing CPUE (Table 2, Figure 4).
A review of Wisconsin shing regulations for the 19 species
revealed variable and somewhat cyclical patterns over time.
Initial shing regulations during the early 1900s for many
species in Wisconsin were liberal (e.g., the rst daily limit for
walleye was 10 sh per day; Figure 5). However, by the mid-
1930s to late 1940s, shing regulations became more restrictive,
in some cases even by today’s standards (Figure 5). Statewide
shing regulations in effect for pansh during this period
remain the most conservative ever enacted for these species
in Wisconsin (e.g., daily limits of 10 for Bluegill). However,
following the conclusion of World War II, angling regulations
for most species were again liberalized. Between 1953 and 1958,
size limits were abolished for Largemouth and Smallmouth bass,
Northern Pike, and Walleye. For a period of time (1960–1965),
there was no daily bag limit or size limit on any pansh species.
Muskellunge was the primary species that retained conservative
Table 2. Mixed eects regression models predicting average size, maximum size, or CPUE based on time (fixed eect) and lake (random
eect) for 19 fishes in Wisconsin over 70 years. Significant regressions denoted by bold and an asterisk. Species where CPUE analyses
were not available are indicated by N.A. Slopes represent predicted rate of change by models (e.g., mm y−1 or log (CPUE) y−1).
Common name Scientific name Period of
record
N (fish) Mean size Max size CPUE
Gamefish PSlope PSlope PSlope
Lake Sturgeon Acipenser
fulvescens
1959–2012 10,022 0.07 4.89 0.03* 6.12* N.A. N.A.
Northern Pike Esox lucius 1946–2012 245,577 <0.0001* 0.63* <0.0001* 1.11* 0.004* −0.004*
Muskellunge Esox masquinongy 1944–2012 54,286 <0.0001* 3.23* <0.0001* 3.38* 0.91 0.00
Smallmouth Bass Micropterus
dolomieu
1944–2012 122,113 0.35 0.23 0.006* 0.83* <0.0001* 0.002*
Largemouth Bass Micropterus
salmoides
1944–2012 276,510 <0.0001* 0.53 0.41 −0.13 <0.0001* 0.002*
Sauger Sander canadensis 1979–2012 8,893 0.87 −0.12 0.08 3.20 N.A. N.A.
Walleye Sander vitreus 1944–2012 453,006 0.002* −0.36* 0.94 −0.02 0.009* −0.005*
Panfish
Green Sunfish Lepomis cyanellus 1945–2012 14,778 0.02* −0.19* <0.0001* −0.83* N.A. N.A.
Pumpkinseed Lepomis gibbosus 1944–2012 79,796 0.56 0.02 0.67 0.03 0.68 0.00
Bluegill Lepomis
macrochirus
1944–2012 154,154 <0.0001* −0.32* <0.0001* −0.42* 0.003* 0.01*
Yellow Perch Perca flavescens 1944–2012 158,568 <0.0001* −0.46* <0.0001* −0.64* 0.85 0.00
Black Crappie Pomoxis
nigromaculatus
1944–2012 122,838 0.001* −0.22* <0.0001* −0.45* 0.98 0.00
Other
Rock Bass Ambloplites
rupestris
1944–2012 80,338 <0.0001* −0.32* 0.0002* −0.33* N.A. N.A.
Black Bullhead Ameiurus melas 1945–2012 16,788 0.14 0.19 0.12 −0.55 N.A. N.A.
Yellow Bullhead Ameiurus natalis 1944–2012 23,575 0.14 −0.20 0.70 0.25 N.A. N.A.
Brown Bullhead Ameiurus nebulosus 1958–2012 8,229 <0.0001* 1.15* 0.04* 0.84* N.A. N.A.
White Sucker Catostomus
commersonii
1945–2012 72,034 0.09 −0.42 0.60 −0.15 N.A. N.A.
Common Carp Cyprinus carpio 1952–2012 14,046 0.20 0.72 0.86 −0.20 N.A. N.A.
White Bass Morone chrysops 1945–2012 7,882 0.12 −0.61 0.72 −0.47 N.A. N.A.
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238 Fisheries | Vol. 41 • No. 5 • May 2016
pieces of evidence corroborate that these declines are real.
First, size trends were not affected by sampling methods.
Though statewide catch records from the 1940s were composed
primarily of fyke netting survey data, by the 1960s a growing
percentage of records from fyke netting had risen to levels
similar to those observed in more recent decades and years
(Figure 2). Even when we exclude data from the 1940s (the fyke
net–intensive period) and rerun all mixed effects regressions,
all of the same species retain their signicance. Additionally,
agreement among trends in both maximum and average size for
all of the pansh species again suggests that modications to
sampling methodologies or personnel are not likely responsible
for the size trends. Maximum size is potentially a size metric
more immune to gear or personnel bias because we are condent
that Wisconsin biologists normally attempted to capture and
measure the largest individuals present. Finally, the use of
gear as a random effect in the size trend models further guards
against any potential gear bias. Second, concordance in the
declining size pattern among a set of similar species that all
had liberal bag limits provides parsimonious evidence that
would be unlikely to occur by chance alone. Third, these results
corroborate those of another study on pansh size trends over a
shorter (24-year) period in Wisconsin for Bluegill, Yellow Perch,
and Black Crappie size structure (Beard and Kampa 1999).
Fourth, similar trends have also been noted in other states (Olson
and Cunningham 1989; Jacobson 2005). Fifth, these results are
statistically conservative (i.e., one would expect older samples,
which had less replication, to have been less likely to have large
maximum sizes—yet the opposite was found).
Based on our review of the history of shing regulations
for pansh in Wisconsin, we suggest that excessive harvest
by anglers is one likely driver for the size structure decline.
Ultimately, we lack adequate harvest data to denitively
demonstrate this, in part because creel surveys have become
exceedingly expensive and challenging for the agency to deploy
on the spatial scales needed. However, it has long been known
that protected or unshed pansh populations opened to angling
quickly undergo large declines in size structure (Coble 1988;
Rypel 2015). For the most part, the history and culture of pansh
angling in Wisconsin has been of a consumptive nature (Beard
and Kampa 1999; Beard et al. 2003). Interestingly, however,
pansh angling regulations were not always liberal. In 1943,
the daily bag limit for Bluegill in southern Wisconsin was 15
with a 152-mm minimum size (Figure 5). Yet this restrictive
period was short-lived, and from 1944 to 1960 the daily bag
limit for pansh species increased to 25. Following 1960–1964,
all harvest restrictions on pansh were lifted (i.e., an unlimited
bag). From 1965 to 1997, an aggregate bag limit of 50 pansh
per day for Bluegill, Pumpkinseed, Black Crappie, White
Crappie P. annularis, and Yellow Perch was implemented. In
1998, the statewide aggregate bag was lowered to 25 pansh per
day. Despite some tightening of regulations since 1965, mean
and maximum sizes of pansh species have declined since the
mid-1940s. Additionally, unlimited bags have been in place
since 1960 for some similar species (e.g., Rock Bass) that have
also undergone substantial size declines (Table 2, Figure 3).
One potential method for improving pansh size structure
in Wisconsin waterbodies would be more restrictive harvest
regulations. There would be a variety of potential management
options available to support such efforts, including reduced
daily bag limits (Jacobson 2005; Rypel 2015), minimum
length limits (Ott et al. 2001), and closed or catch-and-release
shing seasons during spawning (Edison et al. 2006). Though
Figure 4. Long-term trends in the abundance of Largemouth and
Smallmouth bass (boat electrofishing catch per unit eort, CPUE)
in Wisconsin over the last 48 years. Regression lines indicate sig-
nificant mixed eects regression models. Model statistics for any
species can be viewed in Table 2. Error bars represent the mean ± 1
SE. Note that all data are log10-transformed.
shing regulations during this period. Regulations for most
species remained liberal until the 1980s and 1990s. During
the 1980s and 1990s, shing regulations for most gameshes
again began to tighten, but regulations for panshes remained
liberal. Between 1965 and 1998, the daily limit for pansh was
an aggregate bag of 50 sh per day, after which it reduced to
25 per day. The daily bag limit for other species, such as White
Bass, Rock Bass, bullheads, and White Sucker, has remained
unlimited.
DISCUSSION
The size-structure of many sh species in Wisconsin has
greatly shifted over the last 70 years, apparently due to a potent
combination of socioeconomic and shing regulation changes.
We begin our discussion by focusing on two trends that are
emblematic of more general patterns: statewide declines in size,
most clearly seen in pansh species, and statewide increases
in size subsequent to widespread collapse, most clearly seen in
Muskellunge. We then focus on two basic implications of our
ndings: (1) sheries management and conservation applied
over time has been successful and (2) sheries regulations tend
to become cyclical over time. Finally, we provide some of our
views on why big data analyses will become more common and
useful in providing sheries management and policy advice long
into the future.
The Long-Running “Panfish Problem”
“There is nothing better in the food line than a platter of
well-brownedpansh.”
—Carrol 1924
The average and maximum size of pansh species in
Wisconsin has declined signicantly over time. Four main
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Fisheries | www.sheries.org 239
evaluations of these types of regulations have found only modest
improvements to size structure (Ott et al. 2001; Jacobson 2005;
Crawford and Allen 2006; Sammons et al. 2006; Rypel 2015),
exploitation models suggest that large reductions in harvest
are probably needed to produce major improvements (Beard et
al. 1997; Beard and Essington 2000; Mosel 2012). With black
crappie and Yellow Perch, for example, daily bag limits might
need to be seven or fewer sh per angler to cut harvest by more
than 25% (Mosel et al. 2015).
Yet harvest is likely not the only factor affecting pansh size
structure change over time in Wisconsin. For example, Bluegill
size declined signicantly over time while CPUE increased,
suggesting a “stunting” effect that may or may not be related
to harvest dynamics. Habitat changes likely also affect pansh
species and have been widespread over time in the state. For
example, increasing lakeshore residential development has
resulted in depletion of coarse woody habitats that are critical
to various life stages of many pansh species (Jennings et al.
2003; Sass et al. 2006). Nonnative species have also disrupted
ecosystems, prompting shifts in food webs, water clarity, aquatic
macrophytes, habitat availability, and predator regimes—all of
which could provoke growth and size structure change (Lodge
and Lorman 1987; Vander Zanden and Olden 2008; Rypel
2011). Thus, though harvest is probably an important factor
involved with general declines in pansh size, other factors can
also be extremely important. Restoration of Wisconsin pansh
size structure may therefore need to be based on a holistic
approach involving both harvest regulation and ecosystem-
based management. Nevertheless, our retrospective analysis was
ultimately helpful in framing the pansh problem and provided
the science and motivation necessary to initiate a discussion on
potential solutions.
A Return to Big Muskellunge
“Theshermanultimatelyharveststhe(musky)crop,and
payswellfortheprivilege.”
—Wisconsin Department of Agriculture 1945
“Muskellunge are managed as a trophy in Wisconsin. This
means restricting the harvest through relatively high length
limits and low daily bag limits to promote the occurrence of
largeshinthepopulation.”
—Simonson 2012
The Muskellunge is the state sh of Wisconsin. The
philosophy and culture of management and angling for
Muskellunge has changed dramatically over the last 70 years.
Like most Wisconsin gamesh sheries during the early 20th
century, Muskellunge angling was initially harvest oriented
(Threinen and Walker 1958; Simonson 2012). Tourists were
actively encouraged to visit northern Wisconsin lakes to catch
and harvest large and abundant numbers of Muskellunge (see
Figure 5. Timeline of fishing regulations for five major groups of gamefishes in Wisconsin over the last century. Dotted lines indicate periods
with a relatively restrictive regulation and solid lines represent periods with a relatively liberal regulation. Regulations for many species have
been cyclical over time, oscillating between liberal (no size limits, relatively high bag limits) and restrictive (size limits, relatively low bag
limits) harvest regulations.
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240 Fisheries | Vol. 41 • No. 5 • May 2016
above quote from WNDR [1945] promotional shing pamphlet).
Indeed, politicians, wealthy businessmen, and celebrities ocked
to Wisconsin to catch and keep Muskellunge (Figure 2).
High harvest pressure on long-lived predatory species with
low natural densities and biomass turnover rates is unsustainable
(Longhurst 2002; Winemiller 2005; Magurran et al. 2010; Last
et al. 2011; Rypel et al. 2015). Even in the early 1900s, citizens
recognized that Muskellunge harvest needed to be curtailed,
and some of the rst sportshing regulations in Wisconsin
concerned Muskellunge size and bag limits (Figure 5). However,
despite the implementation of harvest limits, Muskellunge size
structure declined from the 1940s through the 1970s. Beginning
in the 1960s, concern rose among a small and vocal group
of anglers and biologists regarding the numbers and sizes of
Muskellunge. These concerns eventually prompted additional
management actions that further reduced harvest to improve
Muskellunge sizes and promote trophy angling. Two of the
most signicant statewide regulatory changes occurred in
1983, with the enactment of a one Muskellunge bag limit and
an 813-mm minimum size, and in 1995, with the enactment
of a 864-mm minimum size. Recently, in 2012, the minimum
size was increased to 1,016 mm, and even larger minimum
size restrictions (e.g., 1,270 mm) are currently moving toward
implementation on select water bodies. Coinciding with (and
perhaps predating and driving) various regulatory changes
was also a rapid rise in the popularity of catch-and-release
Muskellunge angling (Simonson and Hewett 1999; Margenau
and Petchenik 2004; Arlinghaus et al. 2007). For example,
about half of the reduction in observed Muskellunge harvest
over time in Wisconsin could not be explained by regulation
changes and is likely due to voluntary release of legal-sized
sh by avid Muskellunge anglers (Simonson and Hewett 1999).
Sandell (1994) summarized records of Muskies, Inc. member
Muskellunge release rates over time. In 1970, member release
rates of legal-sized sh were 19%. Release rates increased
dramatically to 94% in 1983 and ultimately to more than 99% in
1992. Note especially that following the 1983 regulation change
and coinciding with increased popularity of catch-and-release
shing, mean Muskellunge size began to rise sharply (Figure 3).
Additionally, development of broodstock programs, ngerling
stocking, and habitat management and restoration efforts have
probably also contributed to improvements in Muskellunge
population size structure.
Despite a reduction in harvest over time, angler participation
in the state’s Muskellunge shery has never been higher and
has instead focused on catch-and-release shing. Since 1957,
the number of Muskellunge anglers in Wisconsin has increased
by a factor of 5 to almost 500,000 people in 2010, or 9% of the
total population of the state (Simonson 2012), though the total
number of anglers in the state has increased by only 400,000
during this same period. The increase in anglers shing for
Muskellunge has been accompanied by an increase in the mean
size of this species, and current mean size is now similar to that
estimated from the 1940s. It should be noted that the social will
to achieve these goals emanated from a vocal and active group
of anglers that cared deeply about Muskellunge conservation
(Gelb 2012; Simonson 2012).
Retrospective Key Point 1:
Conservation Management of Fisheries Actually Works
One of the most salient results of this retrospective was
that species for which size structure has either dramatically
improved or remained unchanged have either been those that
have been intensely managed due to high shing pressure or
have been lightly managed due to low shing pressure. Yet in
contrast, size structure of species for which there is a harvest
interest, but where less sheries management has occurred
(e.g., all pansh species), has often declined. We suggest that
these patterns stand as powerful testimony to the effectiveness
of sheries management and conservation when applied over
time. Ultimately, there may be a variety of reasons why pansh
management has been less intensive than gamesh management,
particularly the view that whereas pansh sheries are
consumptive in nature, populations are prolic and do not need
much protection. Even today, when provided with information
on declining pansh sizes, only about 50% of Wisconsin anglers
support stricter statewide pansh regulations designed to
improve size structure (WDNR, unpublished data).
Perhaps the greatest value of this retrospective analysis
was an opportunity to “look in the mirror.” Wisconsin is now
in the process of reformulating the state’s pansh management
plan (dnr.wi.gov/topic/shing/outreach/panshplan.html). As
a component to this process, WDNR personnel have solicited
feedback from anglers through a number of public hearings and
focus groups. Some of the graphs in this article (e.g., Figure 3)
were presented to anglers and stakeholder groups as a part of
this process. These graphs were extremely useful for explaining
the problem of declining pansh sizes and animating angler
discussion and opinions on potential management trade-offs.
We suggest that analogous approaches could be useful for other
species and natural resource management agencies.
Retrospective Key Point 2:
Fisheries Regulations Can Be Cyclical
Recreational shing regulations have to a large extent been
cyclical in Wisconsin (Figure 5). Many of the initial shing
regulations were initially quite liberal. This was followed by a
period of regulation tightening, likely as a response to declining
sheries quality. However, regulations were again liberalized
following the conclusion of World War II. Anecdotally, there
exists a dogma among many managers and scientists that shing
regulations were always liberal during the pre-war era and
have only begun to tighten within the last 30 years. However,
our review of the history of shing regulations in Wisconsin
shows that this is not the case. First, shing seasons have been
in place to protect spawning Northern Pike, Muskellunge, and
Largemouth and Smallmouth bass throughout almost the entire
period of record. Second, it is intriguing that the public long
ago recognized the importance of ecological problems (e.g.,
quality pansh size) and aggregated the political will in the early
1930s to pass a regulation change with the intent of restoration.
Similar circumstances abound today. For example, following
many years of conservative Largemouth and Smallmouth bass
shing regulations, debate is underway on the appropriateness of
liberalizing bass harvest to manage increasingly abundant bass
populations and density-dependent growth (Serns 1982; Hansen
et al. 2015; see also Figure 4).
Cyclical recreational regulations may emerge from two key
drivers. First, shing regulations often change in response to
sh population change. For example, high population estimates
or standing stock biomass can lead to regulations that allow
liberalized harvest (Hilborn and Stokes 2010). Conversely,
low population numbers or standing stock biomass can result
in regulations that reduce total allowable catches or even close
sheries (Roughgarden and Smith 1996). Second, recreational
shing regulations may to a large extent be generational
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Fisheries | www.sheries.org 241
(Cunningham 1993). For example, sheries in Wisconsin
have increasingly been managed for recreation (dnr.wi.gov/
topic/lands/sheriesareas/documents/fmstrategicplan.pdf).
Similar changes in angler demographics and resource usage are
currently being observed in other countries; for example, China
(Aas and Arlinghaus 2009; Goodman and Robison 2013). It is
also of note that regulatory cycles are common features of other
socioeconomic systems regulated by governmental bodies—
most prominently in national and international nancial markets
(Jesus and Gabriel 2006; Repullo and Suarez 2013).
The Rise of Big Data in Fisheries Science
The availability, power, and use of large sheries data sets,
such as that employed in this study, will continue to grow. We
focused on one potential use of these data—the large-scale,
long-term retrospective; however, there are myriad potential
applications. These include studies of macroecological sheries
patterns (Gotelli and Taylor 1999; Rypel 2014), regulation
changes at regional or cross-regional scales (Brousseau and
Armstrong 1987), impacts of conservation on sheries (Lohse
et al. 2008), species distribution and climate change (Lauzeral et
al. 2014), and regional-scale estimations of sheries productivity
(Vert-pre et al. 2013; Esselman et al. 2015). Indeed, many
studies of this variety are underway (Wehrly et al. 2012; Hatten
et al. 2014; Herb et al. 2014), but we think that a clearer vision is
needed to guide the general use of big data in sheries science.
In particular, there is a major need for database standardization
across regions such that patterns operating at larger spatial
scales can also be evaluated (Quist et al. 2009; Levy et al. 2014;
McManamay and Utz 2014).
As big data sheries analyses become increasingly utilized,
it will also become critical to identify specic challenges
associated with the use of different types of data sets. For
example, we often found unknowns when dealing with older
surveys and data sets, a problem that was compounded by
characteristically smaller sample sizes during earlier years.
Though some documentation of specic methodologies,
sampling philosophies, and data collection were tracked down,
this information was inherently vaguer relative to contemporary
surveys. Thus, we feel that this study illustrates the need for
standardization, not only in data management but also in
methods used to collect sheries data in the rst place (Bonar
et al. 2009). Whether moving to a national (or even regional)
standard is “good” or even practical is debatable; however, our
study does highlight the difculties and complexity of dealing
with nonstandardized large databases. Furthermore, certain types
of sheries data might be more or less amenable to conducting
big data analyses to begin with. We focused on documenting
changes to size structure over time in Wisconsin and used CPUE
data as a complement to these patterns (e.g., in understanding
the potential for density-dependent effects). This choice was,
in part, dictated by the inherently variable nature of the CPUE
data (e.g., the high variance around annual means of CPUE;
Figure 4), which may originate from the intrinsic difculty of
estimating sh abundances. For example, CPUE data can be
inuenced by gear type, location, season, catchability, personnel
bias, and randomness (Hayes et al. 2012; Hubert et al. 2012).
Though modern statistical analyses provided mathematical aid
in dealing with issues related to uneven data structures (through
the issue of random effects and covariance structures in mixed
effects models), these underlying issues are undoubtedly
common in most big sheries data sets.
Finally, we have found that a major hurdle in translating big
data sheries analyses into management or policy decisions is
that eld biologists sometimes do not trust the accuracy of the
data. In some cases, we know that this sentiment arises from
personal experience observing specic incorrect values in the
database. Though correcting small data errors is often a quick
technical x, repairing a biologist’s overall trust in the value of
the database can be a longer process. Ultimately, the power of
big sheries data rests in the extraordinary sample sizes and the
ability to swamp out any minor errors that might exist. Despite
the many challenges, manifestation of large-scale sheries
patterns provides compelling evidence of sheries change that
can be useful for advancing sheries management objectives.
Our recommendation is to continue utilizing large data sheries
data sets but with clear and open documentation of the strengths
and weaknesses of these data sets.
CONCLUSIONS
This study provided a statewide 70-year evaluation of size-
structure trends for a large recreational shery. We observed a
variety of extant trends and believe these to be largely a product
of the ways in which various species and ecosystems have
historically been managed. Our retrospective approach was
especially useful in documenting a statewide decline in size
structure of pansh populations over the last 70 years that has in
turn helped galvanize a reexamination of pansh management
policies in the state. For gamesh species, our retrospective
analyses also allowed for an estimation of the degree to
which various conservation management measures have been
successful. We encourage expanded use of large-scale and
long-term databases to document the extent to which sheries
attributes change over space and time in other regions and in
conjunction with sheries management policies. To facilitate
these explorations, we strongly encourage consistent and open
databases that can be combined to promote retrospective and
other analyses at even broader spatial scales.
ACKNOWLEDGMENTS
We acknowledge an army of dedicated and passionate
Wisconsin DNR biologists, managers, technicians, limited-
term employees, supportive staff, and administrators whose
career efforts, data, and dedication over the last 70 years were
compiled into these graphs and analyses. We also especially
thank the Wisconsin DNR pansh team (Jon Hansen, Kurt
Welke, Travis Motl, Dan Hatleli, Patrick Short, Al Niebur, Max
Wolter, and Ron Bruch) whose feedback and efforts greatly have
improved these analyses. Jon Hansen provided constructive
comments on earlier drafts of this article. Steve Gilbert and Greg
Sass provided photographs of a recent DNR fyke netting survey
and the authors with a recent catch of pansh, respectively. Olaf
P. Jensen and three anonymous reviewers provided peer reviews
that greatly improved this article.
FUNDING
This study was funded in part by Federal Aid in Sportsh
Restoration, Project WI F-95-P, Study SSPF. Additional support
for ALR was provided by the National Science Foundation
under Cooperative Agreement #DEB-1440297, NTL-LTER
and by the United States Geological Survey National Climate
Change and Wildlife Science Center grant 10909172 to the
University of Wisconsin-Madison.
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242 Fisheries | Vol. 41 • No. 5 • May 2016
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