Shoal occupancy estimation for 3 lotic crayfish species in the
Tallapoosa River basin, Alabama
by
Molly Ann Moore Martin
A thesis submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Master of Science
Auburn, Alabama
May 14, 2010
Keywords: lotic crayfishes, river regulation, adaptive management
occupancy, detection probability, habitat partitioning
Copyright 2010 by Molly Ann Moore Martin
Approved by
Elise Irwin, Chair, Associate Professor of Fisheries and Allied Aquaculture
J. Barry Grand, Professor of Forestry and Wildlife Sciences
James Stoeckel, Associate Professor of Fisheries and Allied Aquaculture
ii
Abstract
The greatest diversity of crayfishes in the world is in the southeastern United
States; however many species are at risk and lack of information on habitat requirements
and the effects of habitat alteration hamper crayfish conservation efforts (Jones and
Bergey 2007, Taylor et al. 2007). Two priority level 2 species (P2; ADCNR) of crayfish
are endemic to the piedmont region of the Tallapoosa River Basin; Cambarus englishi,
and closely related Cambarus halli, (Schuster et al. 2008). Additionally, widespread
priority level 5 (P5) species, Procambarus spiculifer, have been documented in the region
(Ratcliffe and DeVries 2004). Conservation of native fauna in large rivers is increasingly
dependent on flow management therefore native fauna of the middle Tallapoosa are
potentially strongly affected by flow management employed by Harris Dam (Irwin and
Freeman 2002).
Occupancy was estimated using methods outlined by Mackenzie et al. 2002 for
crayfishes as part of adaptive management of the Tallapoosa River to gain understanding
on how flow dynamics affect biota. Specific objectives were to determine variables
affecting species specific detection probabilities and compare site level occupancy
estimates between regulated and unregulated reaches. Additionally, catch data were
examined for differences in size structure among sites. Lotic crayfishes were collected
from shoals at 3 regulated and 2 unregulated reaches of the Tallapoosa River basin using
pre-positioned area electrofishers (PAE). Detection probability and occupancy were
iii
modeled from presence- absence data as a function of a priori covariates and estimated
in Program PRESENCE using the custom single-season single-species models. Model
selection was based on the principle of parsimony and superfluous models were
eliminated. Weighted model-averaged parameter estimates and unconditional sampling
variances were calculated (Burnham and Anderson 2002). Multiple PAE?s (i.e. spatial
replication; n= 5-20) were collected with habitat characters depth, velocity, percent
vegetation, and substrate composition recorded and used to model detection. Site level
occupancy covariates were based on the a priori hypotheses that occupancy was lower in
regulated reaches due to negative impacts of hydropeaking on recruitment and /or
occupancy varied along a linear downstream recovery gradient from Harris Dam and one
a posteriori hypothesis that occupancy differed among the 5 reaches.
Detection was low for all species in most years which affected precision of
occupancy estimates. A few sites consistently had a high number of detections while
others consistently had few. Variation in number of detections likely reflected changes in
relative underlying populations of crayfishes potentially related to differences in habitat
quality, food quality, number of available refuges, or predation risk. At least one
individual of P. spiculifer, C. englishi, and C. halli were collected from almost every
shoal at least once in the five year sampling period however occupancy estimates varied
spatially and temporally. Modeling results suggested occupancy was similar in regulated
and unregulated reaches of the basin in a ?wet? year while spatial differences were
observed among reaches in all other years. Temporal differences were potentially related
to basin hydrology. Data supports occupancy of P. spiculifer was close to one (? ? 1)
throughout the basin and occupancy of C. englishi was higher in the regulated reaches (?
iv
? 1) than unregulated reaches (? ? 0.50 - 0.60) in most years. Extremely low detection
due to (i.e., sparse data) resulted in model uncertainty making estimates for C. halli
variable and difficult to interpret. Further investigation of distribution and habitat use for
C. halli is warranted and C. halli may be more abundant in tributaries (Ratcliffe and
DeVries 2004). Understanding habitat use of endemic species is important for
recommending management actions directed towards conservation of crayfishes.
Habitat covariates supported predicted biological responses, were sensitive to
annual basin hydrology, and supported evidence of habitat partitioning among species.
Vegetation was important for all species demonstrating a positive effect on detection.
Depth influenced detection probabilities in ?wet? year and velocity influenced detection
in a ?drought? year. Catch data also supported evidence of population level responses to
drought including changes in size structure and potential density reductions and variation
in recovery time among reaches. No evidence supported that the closely related
Cambarus species competitively exclude one another; however, size differences were
observed between species and C. halli may limit their use of shoals in the presence of C.
englishi which may have resulted in consistently low detection of C. halli in our study.
In addition, depth having a strong influence on detection of C. halli and the observed
inverse relation to substrate size between the C. halli and C. englishi may be evidence of
habitat partitioning among these closely related species.
v
Acknowledgements
I would like to thank Dr Elise Irwin and committee for directing my M.S. work
and for their encouragement, support, and guidance. I would also like to recognize Dr.
Jack Feminella, Dr. Carol Johnston, and the late Dr. George W. Folkerts. Their passions
and teachings ignited my interest in aquatic ecology and conservation during my
undergraduate studies at Auburn University. Many thanks to Brain Helms and Emily
Hartfield for their assistance with crayfish identifications and to all of the Alabama
Cooperative Fish and Wildlife Research Unit staff for their field and technical support
they provided during this project, as well as the friendship and encouragement. Lastly, I
would like to extend special appreciation to my best friend and biggest fan who cheered
me all the way through, my husband, Ben.
vi
Table of Contents
Abstract ......................................................................................................................... ii
Acknowledgements ........................................................................................................v
List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii
Introduction ....................................................................................................................1
Methods..........................................................................................................................6
Results ..........................................................................................................................12
Discussion ....................................................................................................................17
Tables ...........................................................................................................................26
Figures..........................................................................................................................35
References ....................................................................................................................49
Appendices ...................................................................................................................55
vii
List of Tables
Table 1.- Water year designation for sampled years in Tallapoosa River basin based on
historical hydrologic information ......................................................................... 26
Table 2.- Site level CPE's for crayfish species by year and overall rank.......................... 27
Table 3. Catch data summary for crayfishes by species and by year ............................... 28
Table 4.- Covariates used to model detection and occupancy of crayfish species ........... 29
Table 5.- Top detection models for crayfish species by year ........................................... 29
Table 6.- Occupancy model set for P. spiculifer with K-L final weights ......................... 30
Table 7.- Occupancy model sets for C. englishi with K-L final weights.......................... 31
Table 8.- Occupancy model sets for C. halli with K-L final weights ............................... 32
Table 9.- Annual reach and basin occupancy estimates (?) and na?ve occupancy for
P. spiculifer ........................................................................................................... 33
Table 10.- Annual reach and basin occupancy estimates (?) and na?ve occupancy for
C. englishi ............................................................................................................. 33
Table 11.- Annual reach and basin occupancy estimates (?) and na?ve occupancy for
C. halli................................................................................................................... 34
viii
List of Figures
Figure 1.- Map of sampling sites in the Tallapoosa River Basin in Randolph, Cleburne,
and Tallapoosa counties Alabama ........................................................................ 35
Figure 2.- Mean catch-per-effort of crayfishes captured in the Tallapoosa River basin
captured between 2005-2009 by season ............................................................... 36
Figure 3.- Mean catch-per-effort of crayfishes captured in the Tallapoosa River basin
captured between 2005-2009 by regulation group................................................ 37
Figure 4.- Carapace length frequency histogram for Procambarus spiculifer captured
between 2005-2009 by regulation group .............................................................. 38
Figure 5.- Carapace length frequency histogram for P. spiculifer captured between 2005-
2009 by season ...................................................................................................... 38
Figure 6.- Carapace length frequency histogram for Cambarus. englishi captured between
2005-2009 by regulation group ............................................................................. 39
Figure 7.- Carapace length frequency histogram for C. englishi captured between 2005-
2009 by season ...................................................................................................... 39
Figure 8.- Carapace length frequency histogram for C. halli captured between 2005-2009
by regulation group ............................................................................................... 40
Figure 9.- Carapace length frequency histogram for C. halli captured between 2005-2009
by season. .............................................................................................................. 40
ix
Figure 10.- Carapace length frequency histogram for C. englishi and C. halli captured
between 2005-2009 ............................................................................................... 41
Figure 11.- Average annual detection probability values by species................................ 41
Figure 12.- Relation between detection and depth for 3 levels of vegetation P. spiculifer
and C. halli. ........................................................................................................... 42
Figure 13.- Relation between detection and velocity with the influence of substrate size
for P. spiculifer. .................................................................................................... 43
Figure 14.- Relation between detection and velocity for 3 levels of vegetation for P.
spiculifer ............................................................................................................... 43
Figure 15.- Relation between detection, velocity, and depth for C. halli ......................... 44
Figure 16.- Relation between detection and percent vegetation with the influence of
substrate size and velocity for C. englishi and unidentified YOY ........................ 45
Figure 17.- Relation between detection and vegetation with the influence of substrate size
for C. englishi....................................................................................................... 46
Figure 18.- Relation between detection and depth with the influence of vegetation and
substrate size for C. halli and C. englishi ............................................................. 47
Figure 19.- Model averaged estimates of reach scale occupancy by year and species ..... 48
1
INTRODUCTION
The greatest diversity of crayfishes in the world is in the southeastern United
States where more than 300 species of approximately 540 species worldwide are extant
(Taylor 2002). However, between 1/3 and 1/2 of crayfish species are at risk of serious
decline or even extinction (Taylor and Schuster 2004). Narrow geographical ranges and
limited distributions increase susceptibility of many southeastern species to extinction.
Furthermore, lack of information on habitat requirements and the effects of habitat
alteration hamper crayfish conservation efforts (Taylor 2002, Jones and Bergey 2007).
Effective conservation of these species will require life history and distribution
information which is available on less than 40% on North American crayfishes (Taylor et
al. 2007). Two priority level 2 species (P2; ADCNR) of crayfish are endemic to the
piedmont region of the Tallapoosa River Basin; Cambarus englishi, and closely related
Cambarus halli, (Schuster et al. 2008). Additionally, 2 widespread priority level 5 (P5)
species, Procambarus spiculifer, and Cambarus latimanus, have been documented in the
region (Ratcliffe and DeVries 2004).
Crayfishes are important members of lotic communities influencing ecosystem
level processes of energy flow and nutrient transfer. When present, crayfish typically
represent the largest biomass of macroinvertebrates (Huryn and Wallace 1987, Rabeni et
al. 1995) and may directly or indirectly influence populations at multiple trophic levels.
Multiple species of predatory fishes, birds, and mammals feed on crayfishes and trophic
2
complexity is increased by the opportunistic feeding of crayfishes on macrophytes, algae,
detritus, macroinvertebrates, amphibians, and fish (Mormot 1995). Molting crayfish are
vulnerable to both intra- and inter-specific attacks and adults frequently cannibalize
juveniles, especially after the maternal pheromone ceases (Rabeni 1985). Crayfishes
perform important ecosystem functions such as influencing sediment movement (Helms
and Creed 2005) and processing macrophytes and leaf litter into fine particulate organic
matter which may be used by other stream organisms (Huryn and Wallace 1987,
Whitledge and Rabeni 1997). Additionally, sensitivity to common classes of pesticides,
organophosphates and carbamates, make crayfish useful bioindicators (Hyne and Maher
2003).
Occupancy, the probability of species occurrence, is a measure used to quantify
the status of a population or community. Changes in occupancy may therefore serve as a
basis for conservation and management decisions (Nichols et al. 1998, Peterson and
Dunham 2003, Gu and Swihart 2004, MacKenzie et al. 2006). Until recently occupancy
estimation procedures did not take into consideration false absence of species or
individuals during surveys (Yoccoz et al. 2001). Mackenzie et al. (2002) developed
modeling techniques using maximum likelihood to estimate occupancy and when
detection probabilities are less than 1. Species detection probability is defined as the
probability of detecting at least one individual of the species during sampling of an
occupied site (MacKenzie et al. 2006). Detection is most often both species and survey
specific and therefore may vary spatially and temporally depending on various factors
such as sampling method, habitat, observer, local extinction and colonization rates,
underlying population size or density, and seasonal behaviors (Mackenzie et al. 2002,
3
Royle and Nichols 2003, Bailey et al. 2004, Hayer and Irwin 2008). Recognizing that
non-detections do not always imply species absence, it is necessary to model variability
in detection probabilities (p) to obtain an unbiased estimate of occupancy (?). Therefore,
simultaneous modeling of detection and occupancy reduces bias by estimating the
probability of absences and failure to detect a species when present.
Conservation of native fauna in large river systems is increasingly dependent on
flow management. Hydropeaking activities decrease persistence of shallow habitats
which facilitate reproduction and YOY (young-of year) survival of various taxa (Freeman
et al. 2001). Therefore, native fauna of the middle Tallapoosa are potentially strongly
affected by flow management employed by R. L. Harris Dam. We modeled detection
and occupancy of crayfishes to help inform adaptive management for the Tallapoosa
River below R. L. Harris Dam which began in 2005. Adaptive management (Walters
1986) is the evaluation of system response to management with emphasis on reduction of
uncertainty to guide further prescribed management. One of the primary objectives for
adaptive management of the Tallapoosa River is to determine faunal response to various
flow management prescriptions. Occupancy was estimated for various taxa, including
crayfishes, to gain understanding on how flow dynamics affect biota.
Objectives of this study were to:
1) Estimate occupancy of crayfish by species in regulated and unregulated river
reaches.
2) Determine variables affecting species-specific detection probabilities.
3) Compare crayfish community and size structure in regulated and unregulated
reaches of the Tallapoosa River basin.
4
Crayfishes exhibit spatial heterogeneity in their distributions due to habitat
preferences (Maude and Williams 1983, Mitchell and Smock 1991, Flinders and
Magoulick 2007) and anti-predatory behavior (Stein and Mangson 1976, Englund and
Krupa 2000, Fortino and Creed 2007, Olsson and Nystrom 2009). Often closely related
species will exhibit disjunct distributions perceived to be a result of competitive
exclusion given the aggressive nature of crayfishes, accordingly species specific habitat
partitioning and ontogenetic shifts in habitat have been observed in crayfishes (Rabeni
1985, Flinders and Magoulick 2007). Crayfish distribution is often related to size
specific predation risk where small crayfish prefer shallow habitat with gravel or small
cobble substrates because the depth and interstitial space prevents predation from fish and
larger crayfish; whereas, larger crayfish prefer slower moving, deeper water to reduce
predation risk from terrestrial predators (Stein and Mangson 1976, Maude and Williams
1983, Mitchell and Smock 1991, Kershner and Lodge 1995 Englund and Krupa 2000,
Flinders and Magoulick 2007, Fortino and Creed 2007). Additionally, demographics
such as body size distribution may correspond to population level processes through
contribution to survival and fecundity (Peters 1983, Werner and Gilliam 1984).
Species detectability is often related to the number of individuals at a site or
within a sampling unit which is likely to be affected by behavior and habitat use or
availability. Season, temperature, and light conditions influence behaviors including
foraging activity, reproduction, and molting cycles (Hobbs 1942, Gore and Bryant 1990,
Bubb et al. 2004). The relation between detection probability and stream habitat features
may provide inference on habitat requirements (Distephano et al. 2003a, Flinders and
Magoulick 2007). The combination of velocity and depth control the distribution of
5
substrate particles and food and therefore influence the distribution of crayfish (Gore and
Bryant 1990). However, amount of cover and behavioral patterns were expected to have
the greatest influence detectability. Specifically, detectability was predicted to exhibit a
positive relation to percent vegetation and velocity because more abundant vegetative
cover may increase the number of individuals in the sampling unit and swift current will
aid in our capture method (see field methods). Detectability was predicted to increase in
low light conditions due to increases in crayfish activity and movement. Variable effects
of substrate type and depth among species and between adults to juveniles were expected
and may reflect habitat preferences. Occupancy estimates were predicted to be lower in
regulated reaches due to negative effects of hydropeaking activities on recruitment
(Freeman et al. 2001), and potentially increase along a downstream recovery gradient
from the dam (Bain et al. 1988, Kinsolving and Bain 1993, Travnichek et al. 1995).
6
METHODS
Study Sites
All sampling sites were located in the piedmont physiographic province of the
Tallapoosa River Basin in East Alabama, USA. The study area contains extensive shoals,
shallow river habitat features that characteristically support high faunal diversity (Irwin
and Freeman 2002). Sample stratification by habitat is often used to sample aquatic biota
that exhibit heterogeneous or clustered distributions (Distefano et al 2003b). Shoals are
ideal study areas in that within a shoal, various microhabitat types (i.e., riffles, runs, and
shallow pools) are represented thus random sampling of randomly selected shoals will
incorporate the variety of available habitats allowing for broader inference in the system.
A probabilistic sampling approach was employed where 5 shoals were randomly
selected from within 5 river reaches that varied in length (Figure 1); 3 regulated reaches
and 2 unregulated reaches for a total of 25 shoals from 5 reaches (Irwin and Freeman
2002). The regulated segment beginning at Harris Dam and terminating downstream in
the headwaters of Martin Reservoir was divided into 3 reaches; Dam to Malone (0-11 km
from dam), Malone to Wadley (11-22 km from dam), and Griffin Shoals to Jaybird
Creek, also known as Horseshoe Bend (60- 90 km from dam). Two unregulated reaches;
Hillabee Creek between Sanford Rd and Hwy 22, and the Tallapoosa River above R.L.
Harris Dam between Ben Mills and Evans Road were monitored to assess how
occupancy varied independent of regulated flows. USGS gages were located near all
7
sites. Annual mean discharge data from the period of record available for each gage was
used to designate water year classification (Table 1).
Field Methods
Crayfish species in temperate regions are mostly active from April- November,
typically breeding in late fall or early spring with females carrying attached young during
April and the beginning of May (Taylor and Schuster 2004). Sites were visited 2 times
per year, once in the summer (late May ? early August) and once in the fall (mid-
September- November). Crayfishes can be difficult to sample and electrofishing is
regarded as an effective method for collecting lotic crayfishes as other methods such as
trapping typically demonstrate sex and size bias, require more visits and potentially gain
less information (Rabeni et al.1997). However, unlike most fishes, crayfishes are not
immobilized from the current and respond with rapid, convulsive backward movement
(Minckley and Craddock 1961) which may have implications about our inability to detect
crayfishes even when present in a sampling unit. Crayfishes were collected using 9m2
(1.5m X 6m) pre-positioned area electrofishers (PAE). PAE?s were left in place
undisturbed for at least 10 minutes and then electrified using alternating current AC
current for 20 seconds (Honda 2.5 GPP; Type VI-A Electrofisher; Smith Root?, Inc.,
Vancouver, Washington) while 2 observers each held one end of a seine along the
downstream border of the PAE and a dip net to capture specimens. Afterward a third
observer captured any visible specimen(s) and then disturbed the substrate dislodging any
additional specimens to be collected in the downstream seine.
Based on previous research (Freeman et al. 2001), 2005-2007 protocol was to
collect 20 samples per visit and in 2008-2009 effort was reduced to 10 samples based on
8
preliminary analysis of fish data (Irwin et al. 2009). Occasionally reduced wetted area
due to low flows prohibited 10-20 samples and in these cases as many samples as wetted
channel would permit were collected. Additionally no samples were obtained from
certain sites in whole seasons due to site accessibility circumstances in year of drought or
because of depth related sampling gear limitations in wet years (see Appendix 1 for
sampling dates). Specimens from 2005-2007 were preserved and brought back to the lab
for processing and 2008-2009 samples were field identified, measured, and released.
Carapace length (CL = tip of rostrum to post-median margin of carapace) was measured
to the nearest 1.0 mm using calipers. Specimens were sexed and identified to species
when possible (Hobbs 1981); individuals smaller than 14 mm in carapace length were not
identified to species and were classified as YOY (young-of-year). Due to erroneous field
identification during the summer of 2008, no specimens were identified to species, and
were simply classified as either adult or YOY based on aforementioned carapace length
criteria.
Specific microhabitat features were measured for each PAE sample. Features
recorded were: depth, velocity, percent vegetation, and substrate composition. Depth and
velocity were measured using a Marsh-McBirney flow staff and meter. Substrate
composition and areal vegetative cover and type were quantified by visual estimation.
Substrate particle designation were recorded in the order of dominance and defined as silt
(<0.1 mm), sand (0.1-1 mm), gravel (0.1 -6 cm), cobble (6 ? 12 cm), boulder (>12 cm),
continuous bedrock, bedrock ledge, and small (1 cm - 4 cm diameter) or large (>4 cm
diameter) woody debris. Additional variables recorded for each PAE were date, time,
water temperature, and weather conditions (i.e., sunny, partly cloudy, or overcast).
9
Data Analysis
Catch data were examined for differences between seasons and regulated versus
unregulated sites in CPE (catch-per effort; crayfish/PAE) using Kruskal Wallis tests and
in carapace length distributions using Kolmogorov - Smirnov tests (SAS v9.1/ SAS
Institute Inc., Cary, NC). Detection probabilities and occupancy were estimated using
maximum likelihood methods modeled as a function of covariates using the logit link
function using single-season, single-species models in Program PRESENCE v. 2.2
(http://www.mbr-pwrc.usgs.gov/software/presence.html, Hines 2006) following the
approach of MacKenzie et al. 2006. To collect occupancy data multiple PAE samples (n
= 5-20; see Field Methods) were taken at each shoal visit; each PAE sample was
considered a sampling occasion (i.e., spatial replication). Each year was considered a
season and each shoal considered a site. Occupancy modeling incorporates 2 types of
covariates; site specific covariates that effect occupancy estimation (?) and survey
specific covariates that effect detection probability estimation (p). Detection and
occupancy covariates modeled were based on the a priori hypotheses that detection
probabilities varied by environmental and habitat characteristics sampled within each
PAE and that occupancy differed between regulated and unregulated segments of the
Tallapoosa, and/or along a linear gradient downstream from Harris Dam. Environmental
and microhabitat features of each sampling occasion (PAE) were used as covariates in
modeling detection probabilities. All variables could be modeled as a continuous
numerical value except light conditions and substrate which were transformed into a
categorical value. Light conditions were expressed binomially (0, high light and 1, low
light; i.e., cloudy or overcast). Substrate particles were converted to a categorical number
10
using values modified from Gore and Bryant 1990, values reflecting the capacity of the
substrate to provide refuge and to alter micro velocities; bedrock and silt = 0, sand = 1,
gravel = 2, cobble = 3, small woody debris = 3.5 bedrock shelf = 4, large woody debris =
4.5, boulder = 5. Gore and Bryant used these values to calculate a roughness index,
however to allow more straightforward interpretation we did not calculate roughness but
used only the largest substrate present in the PAE. Substrate observations were made at
the 9m2 scale which was coarse in relation to the size of crayfishes, therefore using only
the dominant substrate was not considered the most biologically relevant way to represent
the influence of substrate.
Model selection criteria were based on the principle of parsimony; competing
models were compared using Akaike?s information criterion (AIC; Burnham and
Anderson 2002). To limit the number of models in each model set, a 2-step approach
was employed to analyze data. First, detection trials were run to determine which
variable(s) best explained detection probabilities. Then top detection models were then
combined with site level covariates which represented hypothesized differences in
occupancy between sites to create a final candidate model set. Detection and final model
sets were examined for superfluous covariates (i.e., covariates that did not improve model
fit; Burnham and Anderson 2002). Final candidate models with covariates that did not
add substantial model support were eliminated and model weights were re-calculated.
Inference was based on a single ?best? model if the top model weight ? 95; otherwise,
weighted model-averaged parameter estimates and unconditional sampling variances
were calculated on the Kuller- Leiback (K-L) confidence set for the best model created
by simply taking the sum of model weights from largest to smallest until the sum reached
11
? 95 (Burnham and Anderson 2002). Each model set was assessed for lack- of- fit using
the most global model, by calculating Pearson chi- square statistic
Equation 1.
where Oi and Ei are the observed and expected numbers of observations for class i, and n
is the total number of classes defined by the current model, for 100 parametric bootstraps
to determine if the observed statistic for the data set was unusually large (MacKenzie and
Bailey 2004). Substantial lack-of-fit may lead to erroneous inferences resulting from
error in bias or precision of parameter estimates and their associated standard errors.
Variance inflation factor, c-hat, values were used to estimated to identify overdispersion
by dividing the chi-squared statistic by the average observed chi-squared test statistic
from the bootstraps. Overdispersion resulted from small violations in assumptions
however, large c-hat values (>4) suggest inappropriate model structure (Burnham and
Anderson 2002). Overdispersion does not usually increase bias of parameter estimates
but may underestimate error. Therefore, quasi-likelihood analyses criteria (QAIC) were
used to rank any model sets with values of c-hat > 1.
Equation 2.
12
RESULTS
Catch Data and Size Structure
CPEs (catch-per-effort) at individual sites were highly variable ranging from 0 to
3.05 crayfish/PAE. Overall CPEs were lowest in summer 2007 and summer 2008
(Figure 2). Table 2 reports annual site level CPEs and ranks sites over the 5 year period
with catch rates at sites within Hillabee Creek and Horseshoe Bend reaches dropping
dramatically after 2006. Catch data supported that YOY crayfish did not recruit to
sampling gear until reaching the size of approximately 8 mm; catch data from all years of
suggests that this occurs around the second week of June in both regulated and
unregulated reaches. In most years, regulated sites were sampled before June 1st and
when YOY were excluded and all years were pooled no significant differences (X2= 0.15,
DF=1; p=0.47) were observed between CPE at regulated versus unregulated sites (Figure
3). However, in 2009 regulated sites (0.70 crayfish/PAE; CI: 0.40-0.99) had higher (X2=
3.6, DF=; p=0.06;) mean CPEs than unregulated sites (0.22 crayfish/PAE; CI: 0.01-0.43)
YOY excluded.
Over the five year sampling period a total of 1650 crayfish including YOY were
sampled using PAE?s (Table 3). Species identified were: P. spiculifer (n=572), C.
englishi (n=299), and C. halli (n=151). When all data were pooled, P. spiculifer
individuals captured at regulated sites (32.1mm; CI: 31.1mm-33.1mm) were significantly
larger (p < 0.0001) than those from unregulated (27.6 mm; CI: 26.5mm-29.1mm; Figure
4) and individuals captured in the summer (34.5 mm; CI: 33.5.1mm-35.5mm) were
13
significantly (p < 0.0001) larger than those captured in the fall (26.2 mm; CI: 25.1mm-
27.2 mm; Figure 5). When analyzed by year and season, carapace lengths were
significantly different (p<.0001) between regulated (36.2 mm; CI: 34.8 mm ? 37.7 mm)
and unregulated (16.8 mm; CI: 15.5 mm ? 18.3 mm) sites in the summer of 2007.
Additionally, mean body size of P. spiculifer decreased significantly (p < 0.001) between
2006 (26.2 mm; CI: 23.8 mm ? 28.5 mm) and 2007 (17.7 mm; CI: 16.0 mm - 19.4 mm)
at unregulated sites; whereas, mean body size did not significantly differ between years at
regulated sites (p = 0.93).
Carapace lengths of C. englishi captured in regulated reaches (26.3 mm; CI: 25.8
? 26.9 mm) were significantly larger than individuals captured in unregulated reaches
(22.1 mm; CI: 19.8 mm -24.5 mm) when all data were pooled (Figure 6Error! Reference
source not found.). C. englishi captured in the summer (27.2 mm; CI: 26.7 mm ? 27.7
mm) were significantly (p <.0001) larger than those captured in the fall (23.7 mm; CI:
22.6 mm-24.8 mm; Figure 7). When examined by season and year, significant
differences (p <.0001) in carapace length of C. englishi between regulated (27.5 mm; CI:
26.4 mm ? 28.6 mm) and unregulated (16.2 mm; CI: 14.4 mm ? 17.9 mm) were found in
the summer of 2007. When all C. halli data were pooled, significant differences (p <
0.0001) were observed in carapace length distributions between regulated (23.4 mm; CI:
22.1 mm-24.7 mm) and unregulated (19.2 mm; CI: 18.2 mm - 20.2 mm; Figure 8) sites.
However, no seasonal differences (p = 0.14) were observed; therefore seasonal data were
pooled for annual comparisons (Figure 9). In 2007, carapace lengths of C. halli differed
(p <0.0001) between regulated (24.6 mm; CI: 22.0 mm ? 27.2 mm) and unregulated sites
(16.8 mm; CI: 15.6 mm ? 17.9 mm). Additionally, carapace lengths of the 2 Cambarus
14
species were significantly different (p <0.001) where captured specimens of C. englishi
(25.9 mm; CI: 25.3 mm ? 26.4 mm) were on average larger than C. halli (21.3 mm; CI:
20.4 mm- 22.2 mm; Figure 10).
Detection Probabilities and Occupancy
Average detection probabilities were low for all species in most years (p <0.15).
Detection remained fairly constant across years for C. halli (p = 0.03 ? 0.07); however, in
2008 and 2009 detection probabilities for C. englishi and P. spiculifer increased
compared to the other years (Figure 11). Detection probabilities were a function of
habitat variables for all species; habitat covariate values and detection histories are
summarized in Appendices 2-8. Preliminary model results suggested environmental
variables date, temperature, and weather were poor predictors. For example, low light
conditions which were hypothesized to increase detection for all species of crayfishes by
increasing crayfish activity but exhibited a positive relation for one species and a
negative for another or for the same species response would alternate between positive
and negative relation between years. Therefore, environmental covariates were
eliminated from further modeling exercise. Furthermore, dramatic decline of catch rates
in Hillabee Creek and Horseshoe Bend reaches provided the impetus for the a posteriori
site (?) level covariate ?reach? to separate these from their respective regulation groups
(Table 4).
Table 5 indicates the top detection model for each model set and Figures 12-18
demonstrate the relation of species detection to specific habitat characters. Vegetation
positively influenced detection and was identified as an important covariate explaining
detection for all model sets except for P. spiculifer 2008 detection models. When
15
identified as an important covariate, depth consistently had a negative effect on detection
for all species; whereas, the magnitude of the effect differed among species and years
(Figure 12). Depth affected detection of all species in 2005. Velocity had variable
effects on P. spiculifer influencing detection positively in 2008 and negatively in 2009
(Figure 13-14) and demonstrated a positive influence on detection for both Cambarus sp.
And YOY when identified in top detection models (Figure 15-16). Relation and relative
importance of substrate size varied among species (Figures 16-18). Every year, substrate
size was identified as a top covariate positively influencing detection of C. englishi.
When identified in top models substrate size positively influenced detection of P.
spiculifer, but negatively influenced detection of YOY and of C. halli when identified in
top models Additionally, due to known bias in the data of sampling date on YOY capture
histories (i.e., recruitment) YOY data was used to model detection only.
Three data sets were identified as overdispersed; P. spiculifer 2009, C. englishi
2005 and 2009 with variance inflation factor c-hat values of 1.1, 1.2, and 1.3
respectively; therefore QAIC was used to rank these models. Data suggested occupancy
was similar throughout the basin for all species in 2005, whereas potential differences
among reaches were supported for most species in all other years (Table 6- 8). Annual
basin and reach scale occupancy estimates and standard errors are presented by species in
Table 9-11 and Figure 19.
Model certainty was high for P. spiculifer occupancy models allowing inference
in most years to be based on one model. Estimates for P. spiculifer indicate occupancy
was close to one (? ? 1) and fairly stable in the Upper Tallapoosa River and the 2
regulated reaches closest to R. L. Harris dam (i.e., Dam to Malone and Malone to
16
Wadley) although a decrease was observed in the 2 regulated reach estimates in 2008.
Modeling results suggest that occupancy of C. englishi was higher in regulated reaches
(? ? 1) with a slight negative effect of distance from R.L. Harris dam, compared to
unregulated reaches (?? 0.50). However in 2008, estimates were around 60% (?? 0.60)
for all reaches. In the Upper Tallapoosa reaches occupancy estimates for C. englishi
were fairly stable (?= 0.55-0.65), but imprecise and variable in Hillabee Creek.
Estimates for C. halli did not support any consistent pattern of occupancy across sites and
years; however na?ve occupancy and occupancy estimates declined over the sampling
period in Hillabee Creek and exhibited the least variability in the Dam to Malone reach.
17
DISCUSSION
Data and modeling results suggested that occupancy varied by species, reach and
in relation to river regulation and distance from R. L. Harris dam. Detection probabilities
also varied by species and habitat. In addition to providing unbiased estimates of
occupancy, detection probabilities were useful in definition of habitat variables that may
be important to distribution of these species. In addition, length frequency analysis was
useful in identification of recruitment events, species specific population characters and
differences in size structure between populations in regulated and unregulated reaches of
the Tallapoosa basin. Of particular interest to conservation managers was the finding that
occupancy estimates reflected conservation status where the P5 species (of least
concern), P. spiculifer, had highest occupancy throughout the basin and was stable across
years; whereas, data suggested both P2 species (GCN) C. englishi and C. halli may have
more limited distributions. In relation to adaptive management of flow regimes below
the dam, findings will be used to assist managers with decisions relative to future
modifications of the flow regime. The unexpected finding that C. englishi had higher
occupancy below the dam indicated that relations among hydrology, habitat, and
occupancy need to be defined.
In general, the hypotheses related to occupancy for the 3 species were supported
by the data. Modeling results indicated temporal and spatial variation in occupancy.
Occupancy estimates were similar in similar hydrological years with higher occupancy in
18
normal-dry years, 2006 and 2009, than in drought years, 2007 and 2008. In 2005, a
?wet? year, occupancy was constant throughout the basin for all species whereas in other
years spatial variation was exhibited for all three species. In 2007-2009 ?reach? explained
the most variation in occupancy for P. spiculifer and C. halli whereas the 2 regulation
related hypotheses best explained variation in data for C. englishi in most years with
higher occupancy in regulated reaches. Modeling only fall data in 2008 may have
contributed to reduced na?ve occupancy for all species compared to other years as a
portion of the population may have been unavailable to sampling due to seasonal
behaviors. In 2009, P. spiculifer and C. englishi data sets were identified overdispersed;
the overdispersion resulted from unmodeled differences in detection between shoals, with
3 shoals in particular having extremely high detections relative to other sites and years
(Malone B, Malone E and Wadley C) potentially indicating increased local population
size.
Occupancy of P. spiculifer in the basin was likely constant and close to 1.
Although, in 2007-2009 ?reach? explained the most variation in occupancy for P.
spiculifer and in those years Hillabee Creek and Horseshoe Bend had variable and
imprecise estimates due to very low number of detections. Variations in estimates likely
resulted from differences related to habitat or fluctuations in population size. In the
summer of 2009, at Hillabee B no crayfish were detected in PAE samples, but 1 P.
spiculifer was collected in a macroinvertebrate Surber sample and additionally, crayfish
were collected using a backpack electrofishing unit in deeper habitat at Horseshoe Bend
reaches in 2008 and 2009 (Irwin, unpublished data). During 2007-2009, use of shoal
habitats by P. spiculifer and other crayfishes may have differed and/or smaller underlying
19
populations may have been present. These differences were potentially in response to
biotic or abiotic variability and any apparent gradient in occupancy may reflect a gradient
in abundance (MacKenzie et al. 2006). Existing abundance models for occupancy (Royle
et al. 2005) currently are unable to account for temporal variability in the amount of
aquatic habitat, because changes in flow determine the amount and availability of specific
habitats. In addition, relation between habitat amount, persistence and/or stability and
crayfish occupancy have not been quantified.
Similarly to P. spiculifer, changes in occupancy estimates for C. englishi may
reflect changes in population size or shoal use where C. englishi demonstrated higher
occupancy (i.e. shoal use or density) in years with ?normal? mean discharge. Data
indicated that C. englishi have higher occupancy rates and were more widespread in the
regulated reaches. Na?ve estimates and occupancy estimates in the regulated reaches
were similar in similar hydrologic years; highest in ?normal- dry? years and decreasing in
years of ?drought?, but lowest in a ?wet? year. In the ?normal? years occupancy is
estimated to be close to 1 in the regulated reaches. Decreased occupancy in regulated
reaches in 2005 ?wet? years versus other years (?= 0.68) potentially indicated effects of
increased frequency hydropower generation. Additionally, model uncertainty was high in
most years for C. englishi. The inability to determine a best model suggests that the data
were inadequate to make strong inferences potentially due to some ambiguous effect of
parameterization or structure (Burnham and Anderson 2002). Fortunately, information
theoretic approach allows formal multi-model inference in the face of model uncertainty.
Therefore, model averaged parameter estimates based on the observed data were not
conditional to the single observed data set.
20
Despite having the lowest detection probability, model uncertainty was low for C.
halli in 2005-2007, but high in 2008-2009. Estimates for C. halli were difficult to
interpret; however na?ve occupancy declined throughout the sampling period whereas
detection remained relatively constant. Low na?ve occupancy in 2008-2009 was
potentially related to reduced sampling effort for a rare species. Furthermore, the
probability that an occupied site goes undetected with detection probability p = 0.03 is (1-
p)s (s = # of surveys), 54% for 20 sampling occasions and 74% for 10 sampling occasion
respectively. With p this low, models are unable to resolve which sites were
?unoccupied? and which sites were occupied but not detected. Although catch of C.
englishi was higher, occupancy estimates were similar and often higher for C. halli than
C. englishi emphasizing the importance of modeling exercises to avoid bias associated
with raw catch data. To make the best use of data for a species with extremely low
detection such as C. halli (p = 0.03 ? 0.07), a suggested method is to pool data with a
similar species that has a similar detection probability (Mackenzie et al. 2006). However,
detection model results suggested that it would be inappropriate to pool C. halli with any
of the other species as detection probabilities were influenced by different covariates.
Additionally, basin occupancy estimates (i.e., all sites) for C. halli steadily decreased
during the sampling period although detection models and other data support this could
be the result of limited use of shoal habitat.
Habitat covariates were useful in modeling crayfish detection. The relation of
species specific detection to stream habitat features corroborated predicted biological
relations and provided insight on habitat use, supporting evidence of habitat partitioning
among species. Furthermore, results were sensitive to annual variation in basin
21
hydrology and catch data and demographics supported modeling results. Detection
models supported predicted biological relations to habitat characters and differences in
species specific relation to habitat characters indicated differential habitat use among
species. Vegetation had a positive influence on crayfish detection probabilities in all but
one detection model whish supported the importance of vegetation as refuge (Rabeni
1985, Mormot 1995, Distephano et al. 2003a, Flinders and Magoulick 2007, Brewer et al.
2009). Models indicated depth and vegetation were the most important variables
affecting detection of P. spiculifer in 2005-2007; whereas, in 2008 velocity and substrate
were the most important variables. Only fall data was modeled in 2008 and the
difference may be the result of habitat responses related to seasonal changes in flow and
vegetation cover in fall months (i.e., lower flows, less vegetation). However, no change
in detection covariates was observed for the 2 Cambarus species. Additionally, in 2009
top detection covariates for P. spiculifer were velocity and vegetation and detection
demonstrated a negative relation to velocity. Inconsistent selection of important
detection covariates for P. spiculifer could be the result of a generalist habitat use.
Detection of YOY crayfishes was positively related to vegetation and negatively
related to substrate size supporting their preference for gravel substrates because the
interstitial refuge provided prevents predation from fish and larger crayfish (Stein and
Mangson 1976, Flinders and Magoulick 2007, Ollsson and Nystrom, 2009). Detection
probabilities of C. englishi were influenced by vegetation and substrate every year and
consistently demonstrated a positive relation to substrate with higher detection over
larger substrates such as boulders which were common in the 2 upper regulated reaches
where catch rates for C. englishi were consistently highest. Furthermore, C. englishi
22
were detected more frequently at Upper Tallapoosa sites composed mainly of large gravel
substrates than Hillabee Creek which shares similar bedrock and boulder substrate
features of shoals in the mainstem Tallapoosa River. Therefore, data suggested that
presence of boulder substrate habitat does not influence presence or occupancy of C.
englishi, but when present, boulders and other large substrates types may be important
habitat features influencing C. englishi distribution in a lotic system.
The presence of a particular species may affect detection or occupancy of another
species (MacKenzie et al. 2006). Closely related crayfish species often exhibit allopatry
perceived to result from overlap in resource use, however, C. englishi and C. halli are
often sympatric (Bouchard 1978, Hobbs 1981, Ratcliffe and DeVries 2004). Although
differences in carapace lengths reflected in our data suggested that C. englishi were on
average larger than C. halli there was only one shoal where C. halli was never found in
the presence of C. englishi; thus there is little evidence to support that either Cambarus
species competitively excluded one another. However, C. halli may limit their use of
shoals in the presence of C. englishi which may have resulted in lower detection of C.
halli in our study. Depth having a strong influence on detection of C. halli and the
inverse relation to substrate size between the C. halli and C. englishi may be evidence of
habitat partitioning among these closely related species.
Our findings were supported by a recent study in of the 2 species in the Little
Tallapoosa River basin in Georgia reporting that at sympatric sites C. halli was smaller
than C. englishi and shifted habitat use to exploit shallow riffles (Dennard et al. 2009).
Hobbs (1981) reported the contrary, finding when both species were present C. halli did
not use riffles but exploited pools. Larger, ?adult? individuals of C. halli may use pool
23
habitat > 1 m in depth which we did not sample this would support observed differences
in size structure. Observed size differences among the 2 species may also be the result of
asynchronicity in the release of YOYs which has resulted in differing size structures of 2
similar Orconectes spp. (Orconectes luteus and Orconectes punctimanus) in Ozark
streams (Rabeni 1985). However, we did not identify YOY to species, but both of these
factors may have contributed to catch and size differences among the two species.
Crayfish carapace lengths are indicators of population level demographics and
processes and can reflect responses to both biotic processes and abiotic variability.
Seasonal differences in carapace length distributions were the result of a portion of the
population being ?unavailable? for sampling in fall due to behaviors associated with
molting and/or reproduction and from the surge of summer recruits transitioning to small
adults (Taylor and Shuster 2004). In response to drought, Taylor (1988) observed
smaller mean crayfish body size, lower overall density, reduced abundance, and an
increase in the proportion of YOYs. Extremely low water levels during the sampling
period of 2007 in unregulated reaches likely had an effect on crayfish body size as
significantly smaller carapace lengths were observed at unregulated sites for all species in
2007. Smaller size was likely maintained through 2008, but data did not allow these
comparisons due to lack of species specific information in the summer and small to no
sample size for some species in the fall. However, in the summer of 2008 the largest
?adult? captured in the Upper Tallapoosa (n=40) was only 18 mm. This difference was
not observed at regulated sites likely because low water levels are mitigated during
drought by conservation flow management from R.L. Harris Dam.
24
Increased detection probability in 2008 and 2009 may have partially resulted from
a new observer in 2009, but likely also reflect changes in abundance in response to
drought. (Taylor 1988). The proportion of PAEs where C. englishi was detected in 2008
and 2009 was 2 times higher than in 2005-2007 and for P. spiculifer detection were
similar to previous years in 2008 but on average about 40% higher in 2009 than other
years, increasing particularly in regulated reaches in 2009. An increase in detection was
not observed for C. halli, therefore low but consistent detection of C. halli in the sampled
shoal habitat suggested differential and perhaps limited use of shoal habitat for C. halli.
Furthermore, large proportions of YOYs at unregulated sites in 2009 indicated the sites
may still have been in recovery from drought-induced population effects. In 2009,
increased occupancy and particularly high numbers of detections of adults at some
regulated sites, potentially resulted from enhanced recruitment afforded from reduced
hydropeaking activities in previous years. Additionally, in 2008 there were a high
number of detections for YOY at regulated sites. Freeman et al. (2001) reported that
recruitment events in fishes were strongly related to habitat persistence, particularly in
terms of long periods of non-generation below R.L. Harris dam.
Furthermore, detection models supported that variation in annual hydrology
affected crayfish distributions on shoals or microdistribution. Depth influenced detection
probabilities for all species in 2005 which was considered a ?wet? year based on historical
means for the system with a greater proportion of sampling units were in depths > 61 cm
in 2005 than other years. Velocity positively influenced detection of YOY and both
Cambarus species in 2007, classified a ?drought? year and having a greater proportion of
sample units with velocities between 0-10 cm/s than other years. Velocity was also
25
identified in the top detection model for C. halli in 2006 potentially indicating higher
sensitivity to changes in velocities as 2006 was considered a normal to dry year.
Variation in hydrology may have affected crayfish microdistribution; therefore, flow
management has the potential to affect crayfish microdistribution ultimately influencing
metapopulation processes of extinction or colonization. Furthermore, variation in annual
occupancy estimates may also be potentially related to differing hydrology. Multi-season
models using water-year as a covariate to explain extinction/colonization rates could be
developed to test this hypothesis.
Our data demonstrated that flow management implemented by R.L. Harris dam
has the potential to effect crayfish body size, microdistributions, recruitment, and changes
in species occupancy. Occupancy estimates interpreted along with detection results
provided quantitative information useful for evaluating population status and
distributional patterns of crayfishes in the Tallapoosa River basin. Occupancy estimates
for P5 species P. spiculifer was constant and essentially 100%. Furthermore, occupancy
estimates for the P2 species were lower although occupancy of C. englishi was close to 1
in regulated reaches in ?normal? years. Further investigation of distribution and habitat
use for C. halli is warranted and C. halli may be more abundant in tributaries (Ratcliffe
and DeVries 2004) or may exhibit different use of habitat in mainstem versus tributaries.
Understanding habitat use of endemic species is important for recommending
management actions directed towards conservation of crayfishes.
26
Table 1.- Water year designation for sampled years in Tallapoosa River basin based on
historical hydrologic information. Inflow to R.L Harris reservoir includes Upper
Tallapoosa and Little Upper Tallapoosa gages to reflect basin hydrology.
Water Year Designation Inflow R.L. Harrisa Hillabee Classes
2005 Wet 1602.3 422.7
2006 Normal - Dry 967.3 175.7
2007 Drought 476.8 130
2008 Drought 442.1 132.3
2009b Dry-Normal 883.8 275.3
> 1779 > 423 Flood
1556-1779 363-422 Wet
914-1556 230-362 Normal
619-914 133-229 Dry
< 619 < 133 Drought
a USGS 02412000 Tallapoosa River & USGS 02413300 Little Tallapoosa River
for reservoir inflow; USGS 02415000 Hillabee Creek
b Incomplete data used to calculate; data unavailable
27
Table 2. Site level CPE's for crayfish species collected in regulated and unregulated
reaches of the Tallapoosa River basin by year. Annual values include Procambarus
spiculifer, Cambarus englishi, C. halli, but exclude YOY in calculations.
Site 2005 2006 2007 2008 2009
Overall
Rank
Overall Rank
(excluding
YOY)
Dam to Malone A 0.10 0.13 0.20 0.45 0.25 18 20
Dam to Malone B 0.45 0.30 0.50 1.75 1.95 4 2
Dam to Malone C 0.30 0.45 0.75 0.55 1.35 7 4
Dam to Malone D 0.18 0.40 0.18 0.00 0.75 16 13
Dam to Malone E 0.68 0.38 0.55 0.25 0.85 8 5
Malone to Wadley A 0.23 0.35 0.03 0.35 0.55 15 14
Malone to Wadley B 0.08 0.13 0.20 0.60 0.60 14 15
Malone to Wadley C 0.18 1.00 0.70 0.40 1.10 6 3
Malone to Wadley D 0.20 0.53 0.20 0.00 0.55 13 11
Malone to Wadley E 0.20 0.15 0.25 0.05 0.50 20 18
Horseshoe Bend A 0.15 0.30 0.15 0.10 0.20 21 21
Horseshoe Bend B 0.10 0.50 0.00 0.00 0.10 22 22
Horseshoe Bend C 0.40 0.35 0.00 0.15 0.20 19 17
Horseshoe Bend D 0.40 0.00 0.00 0.00 0.00 23 23
Horseshoe Bend E 0.25 0.05 0.05 0.00 0.00 24 24
Hillabee Creek A 0.10 0.35 0.29 0.05 0.20 17 19
Hillabee Creek B 0.00 0.03 0.06 0.00 0.00 25 25
Hillabee Creek C 0.90 0.33 . 0.00 0.10 9 8
Hillabee Creek D 0.50 0.55 . 0.10 0.00 10 7
Hillabee Creek E 0.50 0.50 0.06 0.05 0.00 12 9
Upper Tallapoosa A 0.25 0.23 0.55 . 0.25 3 10
Upper Tallapoosa B 0.15 0.38 0.30 . 0.25 2 12
Upper Tallapoosa C 0.45 0.90 0.70 . 0.95 1 1
Upper Tallapoosa D 0.25 0.30 0.53 1.05 0.25 5 6
Upper Tallapoosa E 0.05 0.18 0.24 0.65 0.15 11 16
28
Table 3.- Catch data summary for crayfishes by species and by year from sites in the
Tallapoosa River basin. Sites are arranged in a longitudinal fashion descending from
Harris Dam. Sites below the solid black line are unregulated sites (Hillabee Creek
between Sanford Road and Alabama Hwy 22 and the Upper Tallapoosa River between
Ben Mills and Evans Ferry). Individuals <14 mm were unidentified and classified as
YOY. Dashes indicate no collections made due to site inaccessibility.
Species Year
Site
P.
spiculifer
C.
englishi
C.
halli YOY Adult* 2005 2006 2007 2008 2009 Total
Dam to Malone A 24 3 1 12 3 12 5 8 13 5 43
Dam to Malone B 65 38 7 46 14 27 13 30 61 39 170
Dam to Malone C 53 32 7 8 5 19 17 31 11 27 105
Dam to Malone D 25 14 6 2 0 9 16 7 0 15 47
Dam to Malone E 39 38 6 5 3 29 15 23 7 17 91
Malone to Wadley A 26 13 0 9 3 12 15 2 10 12 51
Malone to Wadley B 14 20 5 15 1 3 5 16 18 13 55
Malone to Wadley C 25 60 19 39 1 10 41 62 9 22 144
Malone to Wadley D 18 25 5 8 0 9 21 15 0 11 56
Malone to Wadley E 27 4 4 2 0 10 6 10 1 10 37
Horseshoe Bend A 5 4 7 4 0 3 6 4 3 4 20
Horseshoe Bend B 6 1 6 1 0 2 10 0 1 1 14
Horseshoe Bend C 9 8 3 2 0 8 7 0 4 3 22
Horseshoe Bend D 6 2 0 2 0 9 0 0 0 1 10
Horseshoe Bend E 5 2 0 2 0 5 1 1 1 1 9
Hillabee Creek A 21 1 2 6 0 3 16 6 1 4 30
Hillabee Creek B 2 0 0 3 0 1 1 1 1 1 5
Hillabee Creek C 28 3 2 18 0 23 24 - 0 4 51
Hillabee Creek D 29 2 2 11 1 10 32 - 2 1 45
Hillabee Creek E 26 0 5 7 1 14 21 1 1 2 39
Upper Tallapoosa A 19 3 8 86 0 7 77 15 - 17 116
Upper Tallapoosa B 21 1 7 112 0 3 86 9 - 43 141
Upper Tallapoosa C 41 10 27 97 0 9 105 21 - 40 175
Upper Tallapoosa D 19 13 17 59 10 5 60 19 25 9 118
Upper Tallapoosa E 19 2 5 26 4 2 29 7 15 3 56
Total 572 299 151 582 46 244 629 288 184 305 1650
*Note: refers to lack of species specific data for summer 2008.
29
Table 4.- Covariates used to model detection and occupancy of crayfish species collected
from regulated and unregulated reaches of the Tallapoosa River basin.
Detection Occupancy
Depth Regulated
Velocity Distance
Vegetation Reacha
Substrate
aA posteriori model
Table 5.- Top detection models for crayfish species collected with PAE?s from regulated
and unregulated sites of the Tallapoosa River basin.
Species Year Top Detection Model
Procambarus spiculifer
2005 psi(.),p(depth + vegetation)
2006 psi(.),p(depth + vegetation)
2007 psi(.),p(depth + vegetation)
2008 psi(.),p(velocity + substrate)
2009 psi(.),p(velocity + vegetation)
Cambarus englishi
2005 psi(.),p(depth + vegetation + substrate)
2006 psi(.),p(vegetation + substrate)
2007 psi(.),p(velocity + vegetation + substrate)
2008 psi(.),p( vegetation + substrate)
2009 psi(.),p( vegetation + substrate)
Cambarus halli
2005 psi(.),p(depth + vegetation)
2006 psi(.),p(velocity + depth + vegetation)
2007 psi(.),p(velocity + depth + vegetation)
2008 psi(.),p(depth + vegetation + substrate)
2009 psi(.),p(depth + vegetation + substrate)
YOY
2005 psi(.),p(depth + vegetation)
2006 psi(.),p(velocity + vegetation + substrate)
2007 psi(.),p(velocity + vegetation + substrate)
2008 psi(.),p(vegetation)
2009 psi(.),p(vegetation + substrate)
30
Table 6.- Occupancy model set for P. spiculifer collected from regulated and unregulated sites in
the Tallapoosa River basin with K-L confidence model set final weights.
Model AIC ? AIC
AIC
weight K
-2log-
likelhood
Final
weight
2005
psi(.),p(depth + vegetation) 537.07 0.00 0.57 4 529.07 1.00
psi(Regulated),p(depth + vegetation) 539.07 2.00 0.21 5 529.07 -
psi(Distance),p(depth + vegetation) 539.07 2.00 0.21 5 529.07 -
psi(Reach),p(depth + vegetation) 545.07 8.00 0.01 8 529.07 -
psi(.),p(.) 548.73 11.66 0.00 2 544.73 -
2006
psi(.),p(depth + vegetation) 764.67 0.00 0.50 4 756.67 0.90
psi(Regulated),p(depth + vegetation) 766.06 1.39 0.25 5 756.06 -
psi(Distance),p(depth + vegetation) 766.67 2.00 0.18 5 756.67 -
psi(Reach),p(depth + vegetation) 768.44 3.77 0.08 8 752.44 0.10
psi(.),p(.) 799.53 34.86 0.00 2 795.53 -
2007
psi(Reach),p(depth + vegetation) 468.36 0.00 0.86 8 452.36 0.92
psi(.),p(depth + vegetation) 473.28 4.92 0.07 4 465.28 0.08
psi(Regulated),p(depth + vegetation) 474.65 6.29 0.04 5 464.65 -
psi(Distance),p(depth + vegetation) 475.04 6.68 0.03 5 465.04 -
psi(.),p(.) 495.33 26.97 0.00 2 491.33 -
2008
psi(Reach),p(velocity + substrate) 129.24 0.00 0.98 8 113.24 1.00
psi(Distance),p(velocity + substrate) 138.18 8.94 0.01 5 128.18 -
psi(.),p(velocity + substrate) 139.31 10.07 0.01 4 131.31 -
psi(Regulated),p(velocity + substrate) 140.66 11.42 0.00 5 130.66 -
psi(.),p(.) 144.90 15.66 0.00 2 140.90 -
2009a
psi(Reach),p(velocity + vegetation) 316.06 0.00 0.86 8 357.07 0.88
psi(.),p(velocity + vegetation) 321.18 5.12 0.07 4 372.69 0.07
psi(Distance),p(velocity + vegetation) 321.71 5.65 0.05 5 370.93 0.05
psi(Regulated),p(velocity + vegetation) 322.97 6.91 0.03 5 372.44 -
psi(.),p(.) 339.09 23.03 0.00 2 398.76 -
a AIC values replaced by QAIC using variance inflation factor c-hat to adjust for overdispersion
31
Table 7.- Occupancy model sets for C. englishi collected from regulated and unregulated sites in
the Tallapoosa River basin with K-L confidence model set final weights.
Model AIC ? AIC
AIC
weight K
-2log-
likelhood
Final
weight
2005a
psi(.),p(depth + vegetation + substrate) 251.92 0.00 0.50 5 290.31 1.00
psi(Distance),p(depth + vegetation + substrate) 253.17 1.25 0.27 6 289.41 -
psi(Regulated),p(depth + vegetation + substrate) 253.73 1.81 0.20 6 290.07 -
psi(Reach),p(depth + vegetation + substrate) 257.56 5.64 0.03 9 287.47 -
psi(.),p(.) 268.25 16.33 0.00 2 317.09 -
2006
psi(Regulated),p(vegetation + substrate) 351.83 0.00 0.43 5 341.83 0.75
psi(Distance),p(vegetation + substrate) 351.83 0.00 0.19 5 341.83 -
psi(.),p(vegetation + substrate) 355.22 3.39 0.08 4 347.22 0.14
psi(Reach),p(vegetation + substrate) 355.70 3.87 0.06 8 339.70 0.11
psi(.),p(.) 403.12 51.29 0.00 2 399.12 -
2007
psi(Distance),p(velocity + vegetation + substrate) 275.49 0.00 0.29 6 263.49 0.29
psi(Reach),p(velocity + vegetation + substrate) 275.60 0.11 0.27 9 257.60 0.27
psi(.),p(velocity + vegetation + substrate) 275.79 0.30 0.25 5 265.79 0.25
psi(Regulated),p(velocity + vegetation + substrate) 276.35 0.86 0.19 6 264.35 0.19
psi(.),p(.) 299.37 23.88 0.00 2 295.37 -
2008
psi(.),p(vegetation + substrate) 140.90 0.00 0.49 4 132.90 0.85
psi(Regulated),p(vegetation + substrate) 142.26 1.36 0.25 5 132.26 -
psi(Distance),p(vegetation + substrate) 142.88 1.98 0.18 5 132.88 -
psi(Reach),p(vegetation + substrate) 144.42 3.52 0.08 8 128.42 0.15
psi(.),p(.) 155.57 14.67 0.00 2 151.57 -
2009a
psi(Distance),p(vegetation + substrate) 234.68 0.00 0.48 5 292.08 0.48
psi(Regulated),p(vegetation + substrate) 236.08 1.40 0.24 5 293.90 0.24
psi(Reach),p(vegetation + substrate) 236.88 2.20 0.16 8 287.15 0.16
psi(.),p(vegetation + substrate) 237.38 2.70 0.12 4 298.20 0.12
psi(.),p(.) 246.88 12.20 0.00 2 315.75 -
a AIC values replaced by QAIC using variance inflation factor c-hat to adjust for overdispersion
32
Table 8.- Occupancy model sets for C. halli collected from regulated and unregulated sites in the
Tallapoosa River basin with K-L confidence model set final weights.
Model AIC ? AIC
AIC
weight K
-2log-
likelhood
Final
weight
2005
psi(.),p(depth + vegetation) 174.62 0.00 0.53 4 166.62 1.00
psi(Regulated),p(depth + vegetation) 176.62 2.00 0.19 5 166.62 -
psi(Distance),p(depth + vegetation) 176.62 2.00 0.19 5 166.62 -
psi(.),p(.) 178.53 3.91 0.07 2 174.53 -
psi(Reach),p(depth + vegetation) 182.62 8.00 0.01 8 166.62 -
2006
psi(.),p(velocity + depth + vegetation) 356.08 0.00 0.51 5 346.08 0.90
psi(Regulated),p(velocity + depth + vegetation) 357.67 1.59 0.23 6 345.67 -
psi(Distance),p(velocity + depth + vegetation) 357.94 1.86 0.20 6 345.94 -
psi(Reach),p(velocity + depth + vegetation) 360.54 4.46 0.05 9 342.54 0.10
psi(.),p(.) 366.98 10.90 0.00 2 362.98 -
2007
psi(Reach),p(velocity + depth + vegetation + substrate) 248.19 0.00 0.95 10 228.19 1.00
psi(.),p(velocity + depth + vegetation + substrate) 255.39 7.20 0.03 6 243.39 -
psi(Regulated),p(velocity + depth + vegetation + substrate) 256.25 8.06 0.02 7 242.25 -
psi(Distance),p(velocity + depth + vegetation + substrate) 257.39 9.20 0.01 7 243.39 -
psi(.),p(.) 280.26 32.07 0.00 2 276.26 -
2008
psi(Reach),p(depth + vegetation + substrate) 61.52 0.00 0.47 9 43.52 0.48
psi(Distance),p(depth + vegetation + substrate) 63.00 1.48 0.22 6 51.00 0.22
psi(.),p(depth + vegetation + substrate) 63.45 1.93 0.18 5 53.45 0.18
psi(Regulated),p(depth + vegetation + substrate) 64.18 2.66 0.12 6 52.18 0.12
psi(.),p(.) 68.95 7.43 0.01 2 64.95 -
2009
psi(Reach),p(depth + vegetation + substrate) 138.80 0.00 0.47 9 120.80 0.49
psi(.),p(depth + vegetation + substrate) 140.52 1.72 0.20 5 130.52 0.20
psi(Distance),p(depth + vegetation + substrate) 140.64 1.84 0.19 6 128.64 0.19
psi(Regulated),p(depth + vegetation + substrate) 141.51 2.71 0.12 6 129.51 0.12
psi(.),p(.) 145.77 6.97 0.01 2 141.77 -
33
Table 9.- Reach and basin occupancy estimates (?) and na?ve occupancy for P. spiculifer collected from regulated and unregulated
reaches of the Tallapoosa River basin.
2005
2006
2007
2008
2009
Reaches Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE
All Sites 0.96 1 a 0.92 0.99 0.06 0.78 0.85 0.09 0.41 0.42 0.10 0.72 0.78 0.09
Dam to Malone
- -
1.00 0.23
0.99 0.04
0.62 0.51
0.98 0.05
Malone to Wadley
- -
1.00 0.23
0.99 0.04
0.82 0.27
0.98 0.05
Horseshoe Bend
- -
0.96 0.31
0.27 0.46
0 0
0.29 0.47
Hillabee Creek - - 1.00 0.23 0.72 0.52 0 0 0.45 0.48
Upper Tallapoosa
- -
1.00 0.23
0.99 0.04
1.00 0
0.97 0.07
a Variance -covariance not computed successfully
Table 10.- Reach and basin occupancy estimates (?) and na?ve occupancy for C. englishi collected from regulated and unregulated reaches
of the Tallapoosa River basin.
2005
2006
2007
2008
2009
Reaches Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE
All Sites 0.56 0.68 0.12 0.64 0.83 0.12 0.52 0.68 0.13 0.5 0.58 0.13 0.64 0.78 0.11
Dam to Malone
- -
0.97 0.08
0.84 0.30
0.60 0.41
0.98 0.26
Malone to Wadley
- -
0.97 0.08
0.71 0.48
0.59 0.41
0.97 0.27
Horseshoe Bend
- -
0.96 0.10
0.60 0.50
0.53 0.41
0.84 0.43
Hillabee Creek - - 0.52 0.54 0.33 0.43 0.58 0.40 0.47 0.47
Upper Tallapoosa
- -
0.59 0.54
0.58 0.50
0.65 0.36
0.58 0.46
34
Table 11.- Reach and basin occupancy estimates (?) and na?ve occupancy for C. halli collected from regulated and unregulated reaches of
the Tallapoosa River basin.
2005
2006
2007
2008
2009
Reaches Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE Na?ve ? SE
All Sites 0.60 1 A 0.68 0.85 0.12 0.61 0.71 0.12 0.23 0.61 0.29 0.40 0.58 0.15
Dam to Malone
- -
0.87 0.33
1.00 0
0.70 0.42
0.77 0.46
Malone to Wadley
- -
0.84 0.39
0.87 0.15
0.24 0.43
0.66 0.57
Horseshoe Bend
- -
0.83 0.39
0 0
0.79 0.42
0.32 0.42
Hillabee Creek - - 0.84 0.37 0.43 0.23 0.45 0.41 0.24 0.37
Upper Tallapoosa
- -
0.87 0.33
1.00 0
0.93 0.36
0.54 0.59
A Variance -covariance not computed successfully
35
Figure 1.- Map of sampling sites located in the Tallapoosa River basin including
Randolph, Cleburne, and Tallapoosa counties, Alabama. Headwaters are located in
Georgia.
36
Figure 2.- Mean catch-per-effort (# of individuals/pre-positioned area electrofisher) of
crayfishes captured in the Tallapoosa River basin pooled across sites. Top graphs include
P. spiculifer, C. englishi, C. halli, and unidentified YOY; bottom graphs exclude
unidentified YOY.
0.57
1.14
0.73
0.28
0.80
0.26 0.35
0.14
0.55 0.58
0
0.2
0.4
0.6
0.8
1
1.2
CPE
(#
/PA
E)
0.39 0.41
0.50
0.21
0.49
0.23 0.33
0.13
0.39
0.51
0
0.2
0.4
0.6
0.8
1
1.2
2005 2006 2007 2008 2009
CPE
(#
/PA
E)
Year
Summer
Fall
37
Figure 3.- Mean catch-per-effort (# of individuals/pre-positioned area electrofisher) of
crayfishes captured in the Tallapoosa River basin pooled across sites by regulation group.
Top graphs include P. spiculifer, C. englishi, C. halli, and unidentified YOY; bottom
graphs exclude unidentified YOY.
0.33 0.36 0.42
0.46
0.72
0.39
1.13
0.48
0.32
0.69
0
0.2
0.4
0.6
0.8
1
1.2
CPE
(#
/PA
E)
0.26
0.35 0.29 0.31
0.70
0.32 0.37 0.36
0.27 0.23
0
0.2
0.4
0.6
0.8
1
1.2
2005 2006 2007 2008 2009
CPE
(#/
PAE
)
Year
Regulated
Unregulated
38
Figure 4.- Carapace length frequency histogram for P. spiculifer captured between 2005-
2009 in the Tallapoosa River basin by regulation group.
Figure 5.- Carapace length frequency histogram for P. spiculifer captured between 2005-
2009 in regulated and unregulated reaches of the Tallapoosa River basin by season.
0
2
4
6
8
10
12
14
16
18
20
14 19 24 29 34 39 44 49 54
Fre
que
ncy
Carapace Length (mm)
Regulated
Unregulated
0
5
10
15
20
25
14 19 24 29 34 39 44 49
Fre
que
ncy
Carapace length (mm)
Summer
Fall
39
Figure 6.- Carapace length frequency histogram for C. englishi captured between 2005-
2009 in the Tallapoosa River basin by regulation group.
Figure 7.- Carapace length frequency histogram for C. englishi captured between 2005-
2009 in regulated and unregulated reaches of the Tallapoosa River basin by season.
0
5
10
15
20
25
30
14 19 24 29 34 39 44
Fre
que
ncy
Carapace Length (mm)
Regulated
Unregulated
0
5
10
15
20
25
30
14 18 22 26 30 34 38
Fre
que
ncy
Carapace length (mm)
Summer
Fall
40
Figure 8.- Carapace length frequency histogram for C. halli captured between 2005-2009
in the Tallapoosa River basin by regulation group.
Figure 9.- Carapace length frequency histogram for C. halli captured between 2005-2009
in regulated and unregulated reaches of the Tallapoosa River basin by season.
.
0
2
4
6
8
10
12
14
14 19 24 29 34
Fre
que
ncy
Carapace Length (mm)
Regulated
Unregulated
0
2
4
6
8
10
12
14
14 18 22 26 30 34 38
Fre
que
ncy
Carapace Length (mm)
Summer
Fall
41
Figure 10.- Carapace length frequency histogram of 2 species of crayfish collected
between 2005-2009 in regulated and unregulated reaches of the Tallapoosa River basin.
Figure 11.- Average detection probability values calculated from top detection model for
3 species of crayfishes collected from regulated and unregulated sites in the Tallapoosa
River basin.
0
5
10
15
20
25
30
35
40
14 18 22 26 30 34 38
C englishi
C. halli
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
2005 2006 2007 2008 2009
De
tec
tion
(p
)
P. spiculifer
C. englishi
C. halli
42
Figure 12.- Relation between detection and depth for 3 levels of vegetation (low = 0-
30%, medium = 31-60%, and high = 61-100% areal coverage) for 2 species of crayfishes,
(A) P. spiculifer and (B) C. halli, collected from regulated and unregulated sites in the
Tallapoosa River Basin. Data are from 2005 top detection models.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 0.2 0.4 0.6 0.8 1
De
tect
ion
(p)
A
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0 0.2 0.4 0.6 0.8 1
De
tect
ion
(p)
Depth (m)
Low vegetation
Medium vegetation
High vegetation
B
43
Figure 13.- Relation between detection and velocity for 2 classes of substrate for P.
spiculifer collected from regulated and unregulated sites in the Tallapoosa River basin.
Data are from 2008 top detection model with small substrate having values ? 3 and large
indicating values > 3 (see Methods for substrate values).
Figure 14.- Relation between detection and velocity for 3 levels of vegetation (low = 0-
30%, medium = 31-60%, and high = 61-100% areal coverage) for P. spiculifer collected
from regulated and unregulated sites in the Tallapoosa River Basin. Data are from 2009
top detection model.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1 1.2
De
tect
ion
(p)
Velocity (m/s)
Small substrate
Large substrate
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4
De
tect
ion
(p)
Velocity (m/s)
Low vegetation
Medium vegetation
High vegetation
44
Figure 15.- Relation between detection and (A) velocity and depth (B) for C. halli
collected from regulated and unregulated sites in the Tallapoosa River Basin. Data are
from 2006 top detection model where marker color indicates vegetation level (low = 0-
30%, medium = 31-60%, and high = 61-100% areal coverage); the darkest markers
indicating high vegetation.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 0.2 0.4 0.6 0.8 1 1.2
De
tect
ion
(p)
Velocity m/s
A
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 0.2 0.4 0.6 0.8
De
tect
ion
(p)
Depth (m)
B
45
Figure 16.- Relation between detection and percent vegetation with the influence of
substrate size and slow (<20cm/s) and fast ( >21 cm/s) velocities for crayfish collected
from regulated and unregulated sites in the Tallapoosa River basin. Small substrate
includes sand and gravel; large substrate including small and large woody debris,
cobbles, boulders, and bedrock shelf. Data are from 2007 detection models; (A) C.
englishi and (B) YOY.
-0.1
6E-16
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 0.2 0.4 0.6 0.8 1
De
tect
ion
(p)
A
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 0.2 0.4 0.6 0.8 1
De
tect
ion
(p)
Vegetation (%)
Small substrate, slow
Small substrate, fast
Large substrate, slow
Large substrate, fastB
46
Figure 17.- Relation between detection and vegetation for 2 classes of substrate (small
substrates having a value ? 3 and large indicating values > 3; see Methods for substrate
values) for C. englishi collected from regulated and unregulated sites in the Tallapoosa
River Basin. Data are from top detection models of (A) 2006 and (B) 2009. Notice in
2006 detection was more variable and vegetation had a stronger influence whereas in
2009 substrate size had a greater influence.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.0 0.5 1.0
De
tect
ion
(p)
A
0
0.05
0.1
0.15
0.2
0.25
0.3
0 0.2 0.4 0.6 0.8 1
De
tect
ion
(p)
Vegetation (%)
Small substrates
Large substratesB
47
Figure 18.- Relation between detection and depth for 3 levels of vegetation (low = 0-
30%, medium = 31-60%, and high = 61-100% areal coverage) and 2 classes of substrates
for (A) C. halli and (B) C. englishi. Smaller markers plot represent detection values over
substrates with a value ? 3 and larger markers indicate detection values > 3 (see Methods
for substrate values). Data for C. englishi and C. halli are from 2005 and 2009 top
detection models respectively, collected from regulated and unregulated sites in the
Tallapoosa River Basin.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.2 0.4 0.6
De
tect
ion
(p)
A
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 0.2 0.4 0.6
De
tect
ion
(p)
Depth (m)
Low vegetation
Medium vegetation
High vegetation
B
48
Figure 19.- Model averaged estimates of reach scale occupancy by year and species; (A)
P. spiculifer,( B) C. englishi and (C) C. halli.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2005 2006 2007 2008 2009
Dam to Malone Malone to Wadley Horsehoe Bend
Hillabee Creek Upper Tallapoosa
A
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2005 2006 2007 2008 2009
B
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2005 2006 2007 2008 2009
C
49
REFERENCES
Bailey, L. L., T.R. Simmons, and K. H. Pollock. 2004. Estimating site occupancy and
species detection probability parameters for terrestrial salamanders. Ecological
Applications 14(3):692-702.
Bain M.B., J.T. Finn, and H.E. Booke. 1988. Stream flow regulation and fish community
structure. Ecology 69(2):382-392.
Bouchard, R. W. 1978. Taxonomy, distribution, and general ecology of the genera of
North American crayfishes. Fisheries 3(6):11?19.
Boulinier, T., J.D. Nichols, J. D. Sauer, J. E. Hines, and K.H. Pollock 1998. Estimating
species richness: the importance of heterogeneity in species detectability.
Ecology 79 (3): 1018-1028.
Brewer, S. K., R.J. DiStefano, and C.F. Rabeni, 2009. The influence of age-specific
habitat selection by a stream, crayfish community (Orconectes spp.) on secondary
production. Hydrobiologia 619: 1-10.
Bubb, D.H., T.J. Thom, and M.C. Lucas. 2004. Movement and dispersal of the invasive
signal crayfish Pacifisticau leniusculus in upland rivers. Freshwater Biology 49:
357-368.
Burnham, K. P., and D. R. Anderson. 2002. Model selection and inference: a practical
information-theoretic approach. Second edition. Springer-Verlag, New York,
New York, USA.
Comprehensive Wildlife Conservation Strategy (Draft). 2005. Alabama Department of
Conservation and Natural Resources. http://www.outdooralabama.com/research-
mgmt/cwcs/.
Dennard, S., J.T. Peterson, and E.S. Hawthorne. 2009. Life History and Ecology of
Cambarus halli. Southeastern Naturalist 8 (3):479-494.
DiStefano, R.J, J.J. Decoske, T.M. Vangilder and L.S. Barnes. 2003. Macrohabitat
partitioning among three crayfish species in two Missouri streams U.S.A.
Crustaceana 76 (3): 343-362.
50
DiStefano, R.J., C.M. Gale, B.A. Wagner, and R.D. Zweifel. 2003b. A sampling method
to asses lotic crayfish communities. Journal of Crustacean Biology 23(3): 678-
690.
Englund, G. and J. J. Krupa. 2000. Habitat use by crayfish in stream pools: influence of
predators, depth and body size. Freshwater Biology 43:75-83
Flinders C.A., and D.D Magoulick. 2007. Habitat use and selection within Ozark lotic
crayfish assemblages: spatial and temporal variation. Journal of Crustacean
Biology 27 (7):242-254.
Folkerts, G. W. 1997. State and fate of the world?s aquatic fauna. Pages 1?16 in G. W.
Benz and D. E. Collins, editors. Aquatic fauna in peril: the southeastern
perspective. Special publication 1. Southeast Aquatic Research Institute, Lenz
Design and Communications, Decatur, Georgia.
France, R., J. Holmes, and A. Lynch. 1991. Use of sizefrequency data to estimate the age
composition of crayfish populations. Canadian Journal of Fisheries and Aquatic
Sciences 48:2324?2332.
Freeman, M.C., Z.H. Bowen, K.D. Bovee, and E. R. Irwin. 2001. Flow and Habitat
effects of YOY fish abundance in natural and altered flow regimes. Ecological
Applications 11 (1) : 179-190.
Fortino, K. and R. P. Creed. 2007. Abiotic factors, competition or predation what
determines distribution of crayfish in a watershed Hydobiologia 575:301-314
Gore, J.A. and R.M. Bryant. 1990. Temporal shifts in physical habitat of the crayfish,
Orconectes neglectus (Faxon). Hydrobiologia 199: 131-142.
Gu, W. and R. K. Swihart. 2004. Absent or undetected? Effects of non-detection of
species occurrence on wildlife-habitat models. Biological Conservation 116: 195-
203.
Hayer, C.A., Irwin, E.R. Influence of Gravel Mining and Other Factors on Detection
Probabilities of Coastal Plain Fishes in the Mobile River Basin, Alabama
Transactions of the American Fisheries Society 137(6):1606-1620
Helms, B.S and R.P. Creed 2005. The effects of two coexisting crayfish on an
Appalachian river community. Journal of the North American Benthological
Society. 24 (1):113-122.
Hobbs, Jr., H. H. 1942. The crayfishes of Florida. University of Florida Publications,
Biological Science Series No. 3: 1-79.
Hobbs, Jr., H. H. 1981. The crayfishes of Georgia. Smithsonian Institution, Washington.
51
Huryn, A.D. and J.B. Wallace. 1987. Production and litter processing by crayfish in an
Appalachian mountain stream. Freshwater Biology 18: 227-286.
Hyne, R.V., and W.A. Maher. 2003. Invertebrate biomarkers: Links to toxicosis that
predict population decline. Ecotoxicology and Environmental Safety 54:366?374.
Irwin, E.R., and M.C. Freeman. 2002. Proposal for adaptive management to conserve
biotic integrity in a regulated segment of the Tallapoosa River, Alabama, U.S.A.
Conservation Biology. 16(5):1212-1222.
Irwin, E.R.. Kennedy, K.M., Goar, T.P., Martin, B.M. Martin, and M.M. Martin. 2009
Adaptive management and monitoring for restoration and faunal recolonization of
Tallapoosa River shoal habitats: Interim report. http://www.oudooralabama.com/
research-mgmt/State%20Wildlife%20Grants/projectsfunded.cfm
Jones, S.N. and E.A. Bergey. 2007. Habitat segregation in stream crayfishes: implication
for conservation. Journal of the North American Benthological Society 26
(1):134-144.
Kershner, M. W. and D. M. Lodge 1995 Effects of littoral habitat and fish predation on
the distribution of an exotic crayfish, Orconectes rusticus. Journal of the North
American Benthological Society 14(3): 414-422
Kendall, W.L. and G.C. White. 2009 A cautionary note on substituting spatial subunits
for repeated temporal sampling studies of site occupancy. Journal of Applied
Ecology 46:1182-1188.
Kinsolving A.D. and M.B. Bain. 1993. Fish assemblage recovery along a riverine
disturbance gradient. Ecological Applications 3(3):531-544
MacKenzie, D. I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle, and C.A.
Langtimm 2002. Estimating site occupancy rates when detection probabilities are
less than one. Ecology 83 (8): 2248-2255.
MacKenzie, D. I., and L. Bailey. 2004. Assessing the Fit of Site Occupancy Models.
Journal of Agricultural, Biological and Environmental Statistics 9:300?318.
MacKenzie, D. I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines
2006. Occupancy estimation and modeling. Elsevier, Burlington, MA.
Maude, S.H. and D.D. Williams. 1983. Behavior of crayfish in water currents:
hydrodynamics of eight species with reference to their distribution patterns in
southern Ontario. Canadian Journal of Fisheries and Aquatic Science 40: 68-77.
Minckley, W.L. and J.E. Craddock. 1961. Active predation of crayfish on fishes. The
Progressive Fish- Culturist 23: 120-123.
52
Mitchell D.J. and L.A. Smock. 1991. Distribution, life history and production of crayfish
in the James River, Virginia. American Midland Naturalist 126:353-363.
Momot W. T. 1995. Redefining the role of crayfish in aquatic ecosystems. Review in
Fisheries Science 3(1): 33-63.
Nichols, J. D., Boulinier, T, J. E. Hines, K.H. Pollock, and J. D. Sauer 1998. Estimating
rates of local species extinction, colonization, and turnover in animal
communities. Ecological Applications 8 (4): 1213-1255.
Nichols, J. D., Boulinier, T, J. E. Hines, K.H. Pollock, and J. D. Sauer 1998. Inference
methods for spatial variation in species richness and community composition
when not all species are detected
Ollsson K, and P. Nystrom. 2009. Non-interactive effects of habitat complexity and adult
crayfish on survival and growth of YOY crayfish (Pacifastacus leniusculus).
Freshwater Biology 54: 35-46.
Peters, R.H. 1983. The ecological implications of body size. Cambridge University Press,
Cambridge, England.
Peterson, J.T. and J. Dunham 2003. Combining inferences from models of capture
efficiency, detectability, and suitable habitat to classify landscapes for
conservation of threatened bull trout. Conservation Biology 17: 1070-1077.
Poff, N. L., J. D. Allan, M. B. Bain, J. R. Karr, K. L. Prestegaard, B. D.Richter, R. E.
Sparks, and J. C. Stromberg. 1997. The natural flow regime. BioScience 47:769?
784.
Pollock, K. H., J. D. Nichols, T.R. Simons, G.L. Farnsworth, L.L. Bailey, and J. R. Sauer
2002. Large scale wildlife monitoring studies statistical methods for design and
analysis. Evironmetrics 13: 105-119.
Rabeni, C.F. 1985. Resource partitioning by stream dwelling crayfish: the influence of
body size. American Midland Naturalist 113(1):20-29.
Rabeni, C.F., M. Gosset, and D.D. McClendon. 1995. Contribution of crayfish to benthic
invertebrate production and trophic ecology of an Ozark stream. Freshwater
Crayfish 10:163-173
Rabeni, C.F. , K. J. Collier, S. M. Parkyn, and B. J. Hicks. 1997. Evaluating techniques
for sampling stream crayfish (Paranephrops planifrons).?New Zealand Journal
of Marine and Freshwater Research 31: 693?700.
53
Ratcliffe, J. A. and D. R. DeVries. 2004. The crayfishes (Crustacea: Decopoda) of the
Tallapoosa River Drainage, Alabama. Southeastern Naturalist 3:417-430.
Royle, J.A., and J.D. Nichols 2003. Estimating abundance from repeated presence-
absence data or point counts. Ecology 84 (3):777-790.
Royle, J.A., J.D. Nichols, and M. Kery. 2005. Modeling occurrence and abundance of
species when detection is imperfect. Oikos 110:353-359.
Schuster, G.A., C.A Taylor, and J. Johansen. 2008. An Annotated Checklist and
Preliminary Designation of Drainage Distributions of the Crayfishes of Alabama.
Southeatern Naturalist 7(3):493-504.
Stein, R.A., and J.J. Magnuson 1976. Behavioral response of crayfish to a fish predator.
Ecology. 57(4): 751-761.
Taylor, R.C. 1988. Population Dynamics of the crayfish Procambarus spiculifer observed
in different sized-streams in response to drought. Journal of Crustacean Biology
8(3): 40-409
Taylor, C.A. 2002. Taxonomy and conservation of native crayfish stocks. In: Holdich
D.M. editor. Biology of freshwater crayfish. Blackwell Sciences: Oxford pg. 236-
257.
Taylor, C. A. and G. A. Schuster. The Crayfishes of Kentucky. 2004. Illinois Natural
History Survey Special Publication 28
Taylor C. A., G. A. Schuster, J. E. Cooper, R. J. DiStefano, A. G. Eversole, P. Hamr, H.
H. Hobbs III, H. W. Robison, C. E. Skelton, and R. F. Thoma 2007.
Reassessment of the conservation status of crayfishes of the United States and
Canada after 10+ years of increased awareness. Fisheries 32(8):372-389.
Travnichek, V. H., M. B. Bain, and M. J. Maceina. 1995. Recovery of a warmwater fish
assemblage after the initiation of a minimum-flow release downstream from a
hydroelectric dam. Transactions of the American Fisheries Society 124: 836 844.
Walters, C. J. 1986. Adaptive management of renewable resources. Macmillan
Publishing, New York, New York.
Werner, E.E. and J.F. Gilliam. 1984. The ontogenetic niche and species interactions in
size structured populations.
Whitledge G.W., and C.F. Rabeni. 1997. Energy sources and ecological role of crayfishes
in an Ozark stream: insight from stable isotope gut content analysis. Canadian
Journal of Fisheries and Aquatic Science 54:2555-2563.
54
Yoccoz, N. G., J. D. Nichols, and T. Boulinier. 2001. Monitoring of biological diversity
in space and time; concepts, methods and designs. Trends in Ecology and
Evolution 16: 446-453.
55
Appendix 1. Sampling dates for crayfish collection in the Tallapoosa River basin by site and
year. Sites are arranged in a longitudinal fashion descending from R. L. Harris Dam. Sites below
the solid black line are unregulated sites (Hillabee Creek between Sanford Road and Alabama
Hwy 22 and the Upper Tallapoosa River between Ben Mills and Evans Ferry. Dashes indicate no
collections made due to site inaccessibility.
2005 2006 2007 2008 2009
Dam to Malone A 24-Jun 13-Oct 26-May 18-Oct 6-Jun 26-Sep 2-Jul 10-Oct 14-May 16-Sep
Dam to Malone B 27-Jun 12-Oct 25-May 20-Oct 6-Jun 28-Sep 1-Jul 1-Oct 13-May 11-Sep
Dam to Malone C 27-Jun 12-Oct 25-May 20-Oct 6-Jun 28-Sep 1-Jul 1-Oct 13-May 11-Sep
Dam to Malone D 27-Jun 12-Oct 25-May 30-Oct 6-Jun 28-Sep 1-Jul 1-Oct 13-May 11-Sep
Dam to Malone E 27-Jun 12-Oct 25-May 30-Oct 7-Jun 28-Sep 1-Jul 1-Oct 13-May 11-Sep
Malone to Wadley A 20-Jun 20-Oct 31-May 6-Oct 20-Jun 19-Sep 6-Aug 17-Oct 1-Jun 29-Sep
Malone to Wadley B 20-Jun 20-Oct 31-May 6-Oct 20-Jun 19-Sep 6-Aug 17-Oct 1-Jun 29-Sep
Malone to Wadley C 21-Jun 19-Oct 1-Jun 13-Oct 13-Jun 19-Sep 6-Aug 10-Oct 1-Jun 2-Oct
Malone to Wadley D 21-Jun 19-Oct 1-Jun 13-Oct 13-Jun 21-Sep 7-Aug 13-Oct 2-Jun 2-Oct
Malone to Wadley E 21-Jun 19-Oct 1-Jun 13-Oct 13-Jun 21-Sep 7-Aug 13-Oct 2-Jun 2-Oct
Horseshoe Bend A
17-Nov - 6-Nov - 26-Sep 24-Jun 22-Sep 12-Jun -
Horseshoe Bend B - 17-Nov - 6-Nov - 26-Sep 24-Jun 22-Sep 12-Jun -
Horseshoe Bend C - 17-Nov - 6-Nov - 3-Oct 24-Jun 22-Sep 12-Jun -
Horseshoe Bend D - 15-Nov - 23-Oct - 3-Oct 19-Jun 26-Sep 20-Jul -
Horseshoe Bend E - 15-Nov - 23-Oct - 3-Oct 19-Jun 26-Sep 20-Jul -
Hillabee Creek A - 5-Oct 6-Jun 20-Sep 7-Aug 17-Oct 18-Jun 15-Sep 25-Jun -
Hillabee Creek B - 5-Oct 7-Jun 27-Sep 8-Aug 17-Oct 18-Jun 17-Sep 24-Jun 18-Sep
Hillabee Creek C - 5-Oct 7-Jun 27-Sep - - 18-Jun 17-Sep 24-Jun 18-Sep
Hillabee Creek D - 5-Oct 7-Jun 27-Sep - - 18-Jun 17-Sep 24-Jun 18-Sep
Hillabee Creek E - 13-Oct 6-Jun 22-Sep 7-Aug 17-Oct 19-Jun 15-Sep 25-Jun -
Upper Tallapoosa A - 30-Sep 13-Jun 13-Nov 6-Aug - - - 22-Jun 4-Sep
Upper Tallapoosa B - 30-Sep 13-Jun 13-Nov 6-Aug - - - 22-Jun 4-Sep
Upper Tallapoosa C - 30-Sep 13-Jun 13-Nov 6-Aug - - - 22-Jun 4-Sep
Upper Tallapoosa D - 28-Sep 14-Jun 13-Nov 24-Jul 24-Oct 8-Jul 6-Oct 22-Jun 4-Sep
Upper Tallapoosa E - 28-Sep 14-Jun 13-Nov 24-Jul 24-Oct 8-Jul 6-Oct 22-Jun 4-Sep
56
Appendix 2.- Summary of detections for P. spiculifer and number of samples taken in the
Tallapoosa River basin by site and year. Sites are arranged in a longitudinal fashion descending
from R.L. Harris Dam. Sites below the solid black line are unregulated sites (Hillabee Creek
between Sanford Road and Alabama Hwy 22 and the Upper Tallapoosa River between Ben Mills
and Evans Ferry. Dashes indicate no collections made due to site inaccessibility.
2005 Samples 2006 Samples 2007 Samples 2008 Samples 2009 Samples
Dam to Malone A 4 40 4 40 7 40 4 10 2 20
Dam to Malone B 6 40 4 40 7 40 6 10 10 20
Dam to Malone C 7 40 8 40 11 40 3 10 9 20
Dam to Malone D 4 40 8 38 3 40 0 10 7 20
Dam to Malone E 5 40 6 40 10 40 0 10 10 20
Malone to Wadley A 4 40 9 40 1 40 2 10 7 20
Malone to Wadley B 1 40 3 40 1 40 4 10 2 20
Malone to Wadley C 3 40 6 40 8 40 2 10 7 20
Malone to Wadley D 5 40 5 40 3 40 0 10 1 20
Malone to Wadley E 7 40 2 40 7 40 1 10 7 20
Horseshoe Bend A 2 20 2 20 1 20 0 10 0 10
Horseshoe Bend B 1 20 3 20 0 20 0 10 1 10
Horseshoe Bend C 4 20 2 20 0 20 0 10 0 10
Horseshoe Bend D 5 20 0 20 0 20 0 10 0 10
Horseshoe Bend E 3 20 0 20 0 20 0 10 0 10
Hillabee Creek A 1 20 9 40 4 17 0 10 2 10
Hillabee Creek B 0 20 1 40 1 17 0 10 0 10
Hillabee Creek C 9 20 10 40 - 0 0 10 1 20
Hillabee Creek D 4 20 13 40 - 0 0 10 0 20
Hillabee Creek E 7 20 12 40 0 16 0 10 0 10
Upper Tallapoosa A 4 20 7 40 3 20 - 10 2 20
Upper Tallapoosa B 1 20 9 40 3 20 - 10 3 20
Upper Tallapoosa C 2 20 14 40 5 20 - 10 5 20
Upper Tallapoosa D 2 20 5 40 3 30 3 10 1 20
Upper Tallapoosa E 1 20 4 40 4 25 3 10 3 20
57
Appendix 3.- Summary of detections for C. englishi and number of samples taken in the
Tallapoosa River basin by site and year. Sites are arranged in a longitudinal fashion descending
from R. L. Harris Dam. Sites below the solid black line are unregulated sites (Hillabee Creek
between Sanford Road and Alabama Hwy 22 and the Upper Tallapoosa River between Ben Mills
and Evans Ferry. Dashes indicate no collections made due to site inaccessibility.
2005 Samples 2006 Samples 2007 Samples 2008 Samples 2009 Samples
Dam to Malone A 0 40 0 40 1 40 0 10 1 20
Dam to Malone B 6 40 5 40 1 40 6 10 11 20
Dam to Malone C 3 40 4 40 9 40 3 10 4 20
Dam to Malone D 1 40 5 38 3 40 0 10 4 20
Dam to Malone E 12 40 3 40 7 40 2 10 2 20
Malone to Wadley A 5 40 3 40 0 40 1 10 4 20
Malone to Wadley B 2 40 2 40 4 40 2 10 5 20
Malone to Wadley C 3 40 14 40 7 40 4 10 6 20
Malone to Wadley D 3 40 7 40 1 40 0 10 5 20
Malone to Wadley E 0 40 2 40 0 40 0 10 2 20
Horseshoe Bend A 0 20 0 20 2 20 0 10 2 10
Horseshoe Bend B 0 20 1 20 0 20 0 10 0 10
Horseshoe Bend C 1 20 3 20 0 20 2 10 2 10
Horseshoe Bend D 2 20 0 20 0 20 0 10 0 10
Horseshoe Bend E 0 20 1 20 1 20 0 10 0 10
Hillabee Creek A 0 20 0 40 0 17 1 10 0 10
Hillabee Creek B 0 20 0 40 0 17 0 10 0 10
Hillabee Creek C 1 20 1 40 - 0 0 10 1 20
Hillabee Creek D 1 20 0 40 - 0 1 10 0 20
Hillabee Creek E 0 20 0 40 0 16 0 10 0 10
Upper Tallapoosa A 0 20 1 40 0 20 - 10 2 20
Upper Tallapoosa B 0 20 1 40 0 20 - 10 0 20
Upper Tallapoosa C 2 20 3 40 2 20 - 10 2 20
Upper Tallapoosa D 2 20 0 40 3 30 3 10 2 20
Upper Tallapoosa E 0 20 0 40 0 25 2 10 0 20
58
Appendix 4.- Summary of detections for C. halli and number of samples taken in the Tallapoosa
River basin by site and year. Sites are arranged in a longitudinal fashion descending from R. L.
Harris Dam. Sites below the solid black line are unregulated sites (Hillabee Creek between
Sanford Road and Alabama Hwy 22 and the Upper Tallapoosa River between Ben Mills and
Evans Ferry. Dashes indicate no collections made due to site inaccessibility.
2005 Samples 2006 Samples 2007 Samples 2008 Samples 2009 Samples
Dam to Malone A 0 40 0 40 0 40 0 10 1 20
Dam to Malone B 1 40 1 40 2 40 2 10 0 20
Dam to Malone C 1 40 2 40 1 40 0 10 2 20
Dam to Malone D 1 40 3 38 1 40 0 10 1 20
Dam to Malone E 2 40 2 40 1 40 0 10 1 20
Malone to Wadley A 0 40 0 40 0 40 0 10 0 20
Malone to Wadley B 0 40 0 40 1 40 0 10 2 20
Malone to Wadley C 1 40 4 40 8 40 0 10 2 20
Malone to Wadley D 0 40 3 40 1 40 0 10 1 20
Malone to Wadley E 0 40 1 40 3 40 0 10 0 20
Horseshoe Bend A 1 20 4 20 0 20 2 10 0 10
Horseshoe Bend B 1 20 4 20 0 20 0 10 0 10
Horseshoe Bend C 2 20 0 20 0 20 1 10 0 10
Horseshoe Bend D 0 20 0 20 0 20 0 10 0 10
Horseshoe Bend E 0 20 0 20 0 20 0 10 0 10
Hillabee Creek A 1 20 1 40 0 17 0 10 0 10
Hillabee Creek B 0 20 0 40 0 17 0 10 0 10
Hillabee Creek C 2 20 0 40 - 0 0 10 0 20
Hillabee Creek D 1 20 1 40 - 0 0 10 0 20
Hillabee Creek E 1 20 3 40 1 16 0 10 0 10
Upper Tallapoosa A 0 20 1 40 5 20 - 10 0 20
Upper Tallapoosa B 1 20 4 40 1 20 - 10 1 20
Upper Tallapoosa C 2 20 5 40 4 20 - 10 5 20
Upper Tallapoosa D 1 20 4 40 6 30 2 10 1 20
Upper Tallapoosa E 0 20 3 40 1 25 1 10 0 20
59
Appendix 5.- Distribution of depths in sampling units by proportion of samples taken in regulated
versus unregulated reaches by year (A) 2005, (B) 2006, (C) 2007, (D) 2008, (E) 2009 and (F)
distribution of depths in all sampling units in Tallapoosa River basin by year.
0.03
0.41
0.33
0.16
0.070.12
0.54
0.17
0.13 0.05
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
Regulated
Unregulated
0.06
0.57
0.27
0.09 0.02
0.10
0.60
0.20
0.10
0.01
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
0.04
0.52
0.31
0.12
0.01
0.14
0.56
0.22
0.06 0.02
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
0.03
0.59
0.32
0.050.06
0.54
0.34
0.04 0.02
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
0.03
0.61
0.32
0.040.03
0.58
0.26
0.11
0.01
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0-14 15-30 31-45 46-60 > 61
Proporti
on
Depth (cm)
2005 2006 2007 2008 2009
A
C
B
D
E F
60
Appendix 6.- Distribution of velocities in sampling units by proportion of samples taken in
regulated versus unregulated reaches (A) 2005, (B) 2006, (C) 2007, (D) 2008, (E) 2009 and (F)
distribution of velocities in all sampling units in Tallapoosa River basin by year.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0-10 11-20 21-30 31-40 41-50 51-69 61-70 > 71
Proporti
on
Regulated
Unregulated
0
0.1
0.2
0.3
0.4
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0-10 11-20 21-30 31-40 41-50 51-69 61-70 > 71
Proporti
on
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0-10 11-20 21-30 31-40 41-50 51-69 61-70 > 71
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Proporti
on
Velocity (cm/s)
0
0.1
0.2
0.3
0.4
Velocity (cm/s)
2005 2006 2007 2008 2009
A B
C
E
D
F
61
Appendix 7.- Distribution of vegetation (% areal coverage) in sampling units by proportion of
samples taken in regulated versus unregulated reaches (A) 2005, (B) 2006, (C) 2007, (D) 2008,
(E) 2009 and (F) distribution of percent vegetative cover in all sampling units in Tallapoosa River
basin by year.
0.19
0.15 0.15
0.25 0.26
0.54
0.20
0.08 0.05
0.14
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Proporti
on
Vegetation (%)
Regulated
Unregulated
0.31
0.12
0.21 0.17 0.19
0.45
0.12 0.18 0.12 0.13
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Prop
or
tion
Vegetation (%)
0.38
0.25
0.20
0.09 0.07
0.62
0.22
0.12
0.01 0.03
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Prop
orti
on
Vegetation (%)
0.41
0.26
0.17 0.12
0.64
0.16
0.10 0.06 0.04
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Proporti
on
Vegetation (%)
0.12 0.13 0.12 0.12
0.52
0.29
0.14
0.23
0.13
0.22
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Prop
orti
on
Vegetation (%)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0-19 20-39 40-59 60-79 80-100
Proporti
on
Vegetation (%)
2005 2006 2007 2008 2009
A B
C
E
D
F
62
Appendix 8.- Distribution of substrate types in sampling units by proportion of samples taken in
regulated versus unregulated reaches of the Tallapoosa River basin 2005-2009. Data represents
the largest substrate size category present in the sampling unit.
0
0.1
0.2
0.3
0.4
0.5
silt or
bedrock
sand gravel cobble WD shelf boulder
Proporti
on
Regulated
Unregulated