ArticlePDF Available

Antler and Body Size in Black-Tailed Deer: An Analysis of Cohort Effects

Authors:

Abstract and Figures

For long-lived species, environmental factors experienced early in life can have lasting effects persisting into adulthood. Large herbivores can be susceptible to cohort-wide declines in fitness as a result of decreases in forage availability, because of extrinsic factors, including extreme climate or high population densities. To examine effects of cohort-specific extrinsic factors on size of adults, we performed a retrospective analysis on harvest data of 450 male black-tailed deer ( Odocoileus hemionus columbianus ) over 19 years in central California, USA. We determined that population density of females had a more dominant effect than did precipitation on body size of males. Harvest of female deer resulted in increases in the overall size of males, even though a 6-year drought occurred during that treatment period. Body size was most influenced by female population density early in life, while antler size was highly affected by both weather early in life and the year directly before harvest. This study provides insights that improve our understanding of the role of cohort effects in body and antler size by cervids; and, in particular, that reduction in female population density can have a profound effect on the body and antler size of male deer.
This content is subject to copyright. Terms and conditions apply.
Research Article
Antler and Body Size in Black-Tailed Deer: An Analysis of
Cohort Effects
Johanna C. Thalmann,1R. Terry Bowyer,1Ken A. Aho,1
Floyd W. Weckerly,2and Dale R. McCullough3
1Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello, ID 83209, USA
2Department of Biology, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
3Department of Environmental Science, Policy, & Management, University of California, Berkeley, 130 Mulford Hall No. 3114,
Berkeley, CA 94720, USA
Correspondence should be addressed to Johanna C. almann; thaljoha@isu.edu
Received  August ; Revised  October ; Accepted  November 
Academic Editor: Sveinn Are Hanssen
Copyright ©  Johanna C. almann et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
For long-lived species, environmental factors experienced early in life can have lasting eects persisting into adulthood. Large
herbivores can be susceptible to cohort-wide declines in tness as a result of decreases in forage availability, because of extrinsic
factors, including extreme climate or high population densities. To examine eects of cohort-specic extrinsic factors on size of
adults, we performed a retrospective analysis on harvest data of  male black-tailed deer (Odocoileus hemionus columbianus)
over  years in central California, USA. We determined that population density of females had a more dominant eect than did
precipitation on body size of males. Harvest of female deer resulted in increases in the overall size of males, even though a -year
drought occurred during that treatment period. Body size was most inuenced by female population density early in life, while
antler size was highly aected by both weather early in life and the year directly before harvest. is study provides insights that
improve our understanding of the role of cohort eects in body and antler size by cervids; and, in particular, that reduction in
female population density can have a profound eect on the body and antler size of male deer.
1. Introduction
Climate and population density inuence organisms
throughout their lives and oen lead to phenotypic variation
among cohorts. Cohort eects occur when individuals born
within the same year experience similar environmental
conditions, which may alter the life-history characteristics of
that group []. Large mammalian herbivores, in particular,
are strongly inuenced by local weather, because of direct
eects of weather on forage [–]. Rainfall increases the
quality and quantity of vegetation [], which has a positive
inuence on body size of some ungulates [–]. Body size is
aected more easily by variation in the environment when a
population is at moderate to high densities [–], because of
increased intraspecic competition for food [].
Understanding how the interaction between climate and
population density inuences body mass throughout the life-
time of a large herbivore enables biologists to better predict
growth, survival, and reproduction within cohorts [–].
Extrinsic and intrinsic factors aect body condition, and
when resources are limited, important tradeos may be made
earlyinlifebyanindividual,orbyamotherbeforeparturi-
tion, which aects ospring while in utero [].osetrade-
os result in delayed life-history characteristics that can per-
sist into adulthood and even through future generations [].
In times of severe weather or high population density,
tradeos are made by the mother when the fetus is in utero
[]. Decreases in maternal investment during periods of low
spring precipitation stunt the growth of fetuses, resulting in
young that are born small and remain small throughout their
lives [–]. Furthermore, much of the variation in adult
size of ungulates has been linked to conditions experienced
during the year of birth [] and environmental conditions
and nutrition of their grandmothers  generations previously
[, ].
Hindawi Publishing Corporation
Advances in Ecology
Volume 2015, Article ID 156041, 11 pages
http://dx.doi.org/10.1155/2015/156041
Advances in Ecology
Antler size has been positively correlated with warmer
temperatures and higher precipitation in spring and autumn
during the year of harvest []. Discerning the inuence
of extrinsic factors the year preceding harvest with those
that occurred during youth is critical for understanding
phenotypic variation in size of antlers. Determining the
degree to which weather and population density during youth
and adulthood aect antler and body size of ungulates is still
poorly understood, although body and antler size are related
to a number of life-history characteristics among cervids [].
We conducted a retrospective analysis of harvest data []
to examine eects of environmental conditions on antler and
body size within and across cohorts of a population of black-
tailed deer (Odocoileus hemionus columbianus)fromto
. We hypothesized that cohort eects would inuence
body and antler size of male black-tailed deer. Specically,
we predicted that high annual precipitation during gestation,
duringtherstyearofgrowth,andduringtheyearofharvest
would be related to body and antler size. Temperatures that
were low in summer and high in winter during those periods
were predicted to positively aect body size of males. Also, we
predictedthatadecrease(broughtaboutbyharvest)infemale
population density during gestation, and  year following
parturition,wouldresultingreaterbodyandantlersizeof
males at the time of harvest. Understanding how a single year
of environmental factors aects life-history characteristics of
multiple generations can lead to improvements in predicting
body growth and antler size, as well as providing insights into
conserving and managing those large herbivores.
2. Methods
Research by McCullough [] was conducted at the Hopland
Research and Extension Center (hereaer Hopland) of the
University of California (󸀠N, 󸀠W). e , ha
areaislocatedinMendocinoCounty,aboutkmnorthof
San Francisco, in the central portion of the coastal mountain
ranges of California, USA. Hopland is characterized by a
Mediterranean climate; mean summer and autumn temper-
atures range from  to C, and winters are mild with
occasional frost in valley bottoms and infrequent light snow
at high elevations (> m). Interannual rainfall was variable
among years (–; 𝑥=96mm; range = – mm, CV
= %), with a -year drought occurring during the treatment
period (–), where females were harvested (Figure ).
Discrete seasons of vegetative growth and senescence
were evident between  and , which followed seasonal
patterns of precipitation (𝐹12,12 = 2.69,𝑃 = 0.01). Most
precipitation occurred from October through April; rain
seldom fell during May through September, which also was
characterized by the warmest average monthly temperatures
(Figure ). Winters are usually mild and wet, encouraging
herbaceous growth that extends from November through
April. e percentage of vegetative cover changes as wet
conditions shi towards the dry season, with an increase in
annual grasses and decrease in forbs []. Common vegetation
typesonHoplandincludeoakwoodlands(Quercus spp.),
grasslands (e.g., Bromus spp., Erodium spp., and Festuca spp.),
and chaparral (Adenostoma spp.) [].
Total annual precipitation (mm)
Ye a r s
Pretreatment Treatment Posttreatment
Long-term average
2000
1500
1000
500
0
1980 1985 1990 1995
F : Total annual precipitation for all years of the study (–
) is depicted for Hopland California, USA. Yearly precipitation
wascalculatedfromthestartofJulytotheendofJuneofthe
following year. is arrangement allowed for the examination of
a complete year of vegetative growth, in particular during the wet
season (October–April). Darker blue represents the experimental
years when both females and males were harvested (𝑥 = 737.5mm,
SD = . mm). Light blue is pretreatment (only male deer
harvested) (𝑥 = 1092.7 mm, SD = . mm), and diagonal cross-
hatching represents posttreatment (only male deer harvested) (𝑥=
1178.5mm, SD = . mm). e dark red dashed line identies
the long-term average annual precipitation (𝑥 = 932.0 mm, SD =
. mm).
Total precipitation (mm)
Tot a l
precipitation
Average
temperature
Average temperature (C)
Months
Jan.
Feb.
Mar.
Apr.
May.
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
100
80
60
40
20
0
200
150
100
50
0
F : Climate graph illustrating the relationship between
temperature and precipitation for Hopland, California, USA. Tri-
angles represent average monthly temperature and circles depict
average total monthly precipitation. Diagonal lines represent times
of drought (May–September), whereas the cross pattern represents
times of water surplus (October–April). Data were collected from a
local weather station at Hopland from  to .
Domestic sheep (Ovis aries)werepasturedonHopland
and maintained at a population size of –, females
throughout the study (–) []. Black-tailed deer
were abundant and hunted annually. e prehunt population
before the study began was estimated at  deer (.
deer/km2) []. In , at the end of the treatment period
Advances in Ecology
(female and male harvest), the population was approximately
 deer (. deer/km2). Predators of deer include bobcats
(Lyn x rufu s), coyotes (Canis latrans), mountain lions (Puma
concolor), and black bears (Ursus americanus). No attempt
wasmadetocontrolpredatorsduringthestudy.
2.1. Collection and Measurements of Deer. To determine if
density-dependent eects inuenced antler growth and body
size of male black-tailed deer, we reanalyzed data collected
by McCullough [] from  to . No data were used
frommalessampledin,however,becausetoofewmor-
phometric measurements were collected. During that study,
there was a -year treatment period (–) where a mean
of  females (SD = . females) were harvested each year
in addition to the annual harvest of males. Six years before
and aer the treatment period of female harvest, only male
deer were killed ( males per year). e initial research by
McCullough [] was conducted under animal use protocols
approved by the University of California, Hopland Research
and Extension Center, and a memorandum of understanding
and collecting permits from the California Fish and Game
Commission.
In our analysis, we used weight, antler measurements, and
estimatedagefrommaledeerthatwereharvestedduring
–. Males were not included if they lacked data for
ageormultiplemeasurementsofantlers.Whenonlyasingle
antler measurement (diameter or length of either the right
or le antlers) was missing, data were interpolated (𝑛=49)
from the complete data set. We used the regression function
in the R .. statistical environment [, ] to determine
those values. Dressed weight was used in place of whole-body
weight because whole weights were not recorded from  to
. We used this approach because dressed and whole-body
weights [] of black-tailed deer exhibited a strong positive
relationship (𝑟2= 0.91,𝑃 < 0.001,̂
𝑦 = 6.5573 + 1.739𝑥).
Moreover, carcass weight in mule deer (O. h. hemionus)is
strongly correlated with other body measurements, including
chest girth, body length, head length, and mandible length
[]. From  to , deer were aged from either cementum
annuli or tooth eruption and wear []. Data on cementum
annuli were used for aging deer during years where both
typesofdatawereavailable.Wewereabletousebothtypes
of data because age determined from cementum annuli and
tooth eruption and wear was positively related (𝑟2=0.63,
𝑃 < 0.001,̂
𝑦 = 0.1589 + 1.0303𝑥). Aging black-tailed
deer based on tooth eruption and wear is highly accurate
through . years of age. Approximately % of deer . to
. years of age were categorized correctly with a tendency
to underestimate ages [].
To reduce the number of variables, a Principal Compo-
nent Analysis (PCA) based on the covariance matrix was
conducted on antler measurements (right and le diameter
and length, and spread) collected by McCullough []. Spread
and length were measured to the nearest . mm, and
diameters were measured to the nearest .mm. e number
of le and right antler tines (points) was not included in
the PCA, because of problems associated with the use of
a small range of meristic characters to estimate phenotypic
development []. e proportion of variance explained by
the PCA was nearly identical between the whole raw data set
(.) and with the addition of interpolated values (.);
consequently, we included the interpolated values to increase
sample size (𝑛 = 450). PC for antler measurements had
negative loadings, with larger antlers represented by negative
values and smaller antlers by positive values. e proportion
of variance explained by PC was .. Loadings for antler
measurements were similar (Table ), indicating that PC was
a reliable measure of antler size [, ]. We multiplied the
principal components by negative  for all analyses so positive
values represented larger antlers, making results more logical
to follow.
Ageisrelatedtoantlerandbodysizeinmalecervids[,
]. We tted the Von Bertalany growth curve to examine
the relationship between body size and age in male black-
tailed deer using the package Fishmethods [] in R [, ].
is procedure also provided the best t for the relation
between age and antler size (PC), because we had few old
individuals that might have resulted in a quadratic curve [].
Residuals were obtained for both antler and body size, which
corrected those size variables for age.
2.2. Local Weather. We determined eects of extrinsic factors
on body weight and antler size of males by compiling local
weather data. Total precipitation and average temperature
were collected at Hopland each month. Monthly average
temperatures and total monthly precipitation were compared
in a Walter climate graph (Figure ) to distinguish seasons of
vegetative growth (October through April) and times of water
decit (May through September) [].
Periods for which weather data were considered in
models included gestation by the mother while the male was
in utero (October through April); the rst year of growth
by the male ospring (May through April); and the weather
duringtheyearbeforeharvest(Octoberthepreviousyear
through September). For both the rst year of growth and
theyearbeforeharvest,wedividedthoseperiodsintowet
(October through April) and dry (May through September)
seasons for a clearer understanding of how temperature and
precipitation inuenced deer. For instance, weather variables
thatwouldbeincludedindataforamaledeerharvestedat
. years of age in autumn of  would include weather
experienced while in utero during gestation by the mother
(October  through April ), the dry season of the rst
year of growth by the male (May  through September
), the wet season of the rst year of growth by the male
(October  through April ), the wet season of the
year preceding harvest (October  through April ),
and the dry season of the year preceding harvest of the
male (May  through September ). Although the last
third of gestation is the most energetically expensive for large
mammalian females [], weather during full gestation was
used in our analyses, because female body condition aects
the ospring throughout its time in utero.Furthermore,
female deer inhabiting a Mediterranean ecosystem must
build sucient body reserves early in gestation, because
parturition occurs at the beginning of the dry season when
vegetation starts to senesce [, ].
Advances in Ecology
10
20
30
40
50
60
70
Age (years)
Body size (kg)
02468101214
Pretreatment
Tre atm e nt
Posttreatment
F : e Von Bertalany growth curve depicts age at mean
asymptotic body mass of male black-tailed deer during the study.
Pretreatment, treatment, and posttreatment categories represent the
period when males were harvested in Hopland, California, USA,
from  to .
2.3. Statistical Analyses. Intrinsic factors (body and antler
size) were modeled as dependent variables in R [] against
extrinsic variables that included treatment period (pretreat-
ment and combined treatment and posttreatment) and total
monthly precipitation and average monthly temperature for
each time period (i.e., in utero, the dry season during the
rst year of growth, the wet season during the rst year of
growth, the wet season before harvest, and the dry season
before harvest). Treatment and posttreatment were combined
into a single period because of lingering density-dependent
factors aer  years of female harvest. An interaction term
between precipitation and population density was added
to models. Body weight corrected for age was included as
avariableintheantler-sizemodel,becausehigherbody
reserveswouldallowthemaletoinvestmoreinantler
growth [, ]. Contrarily, antler size was omitted from
the body-size model because antler growth is less likely to
aect body size. Colinearity was examined for all variables
with variance ination factors (VIF) in R package Car [].
All continuous quantitative variables had VIF values
indicating that multicolinearity did not bias our models
[].WeusedthepackageMuMIn[]inRtodredgeand
average the top models based on all models with values
 using Akaike’s information criterion for small samples
(AICc) of the top model []. Condence intervals for
model-averaged parameters were lowered to % to more
closely conjoin the information theoretic approach of AICc to
most frequentist condence intervals. In particular, holding
likelihood constant, AICc will increase by 2𝑑,where𝑑is the
number of added parameters in a model. us, just as 𝑃(𝜒2
1
1.962) ≈ 0.95,where𝜒2
1is a chi-square distribution with one
degreeoffreedom,itisalsotruethat𝑃(𝜒2
1≤ 2) ≈ 0.85
Body size (kg)
10 20 30 40 50 60 70
Antler size (PC1)
6
4
2
0
2
4
6
8
10
12
Large
Small
r2= 0.505
P < 0.001
̂
y = −7.252 + 0.197x
F : Body weight compared with antler size for male black-
tailed deer during –, Hopland, California, USA.
[]. We used multiple lines of inquiry to make the most
informed decision about which variables were most inuen-
tial in determining body weight or antler size. We examined
% condence intervals, and the relative importance of each
variable,calculatedasasumoftheAkaikeweightsoverall
of the models in which a particular parameter appeared. To
further understand how a reduction in female population
density aected antler and body size, we performed the
median test []. We compared the median before and aer
female harvest began with the median for the entire study.
3. Results
We performed a retrospective analysis on data for  black-
tailed deer, harvested between  and  at the Hop-
land Research Extension Center. Male black-tailed deer, on
average, weighed . kg. Asymptotic body mass, determined
from Von Bertalany growth equation, was  kg at . years
of age (𝐿𝑡= 46.005(1 − 𝑒−0.6117(𝑡 + 1.4106)), 𝑟2= 0.26;
Figure ). To account for the relationship between age and
body and antler size, residuals from the Von Bertalany
growthcurvewereusedinsubsequentanalyses(Figure).
Dressed weight and antler size (PC) also were positively
related (Figure ), but to better understand changes in size
of male deer, we examined those variables separately.
Body size of male deer increased during the study
(Figure(a),Table).Bodysize(i.e.,dressedweightcorrected
for age) increased profoundly aer the treatment period but
was highly variable among individuals ( ̂
𝑦 = 0.44𝑥 − 877.60,
𝑟2= 0.13,𝑃 < 0.001; Figure (a)).
Deer harvested aer the treatment period began were
signicantly larger than those harvested during the pre-
treatment period. When corrected for age,  of  male
deer harvested aer the treatment period had a body weight
greaterthanthemedianfortheentiresamplesize(𝑛 = 450;
𝜒2= 14.22,𝑃 > 0.001).roughthestudy,growthrateand
asymptotic body weight of male deer rose from pretreatment
(. per year; . kg) to posttreatment period (. per
Advances in Ecology
Ye a r
1980 1985 1990 1995
Age 2.5 Age 2.5
Age 3.5
Age 4.5
30
20
10
0
−10
−20
−30
Body weight (kg)
corrected for age
Pretreatment Treatment Posttreatment
(a)
Pretreatment Treatment Posttreatment
Ye a r
1980 1985 1990 1995
Age 2.5
Age 2.5
Age 2.5
Age 3.5
12
10
8
6
4
2
0
−2
−4
−6
Antler size (PC1)
corrected for age
(b)
F : Trends in size of male black-tailed deer in Hopland, California, USA. (a) e increase in body size (dressed weight corrected
for age) between years. (b) e increase in antler size (principle component  corrected for age) through time. Sections are partitioned by
pretreatment (only males harvested, –), treatment (both males and females harvested, –), and posttreatment (only males
harvested, –). Arrows point to the four largest individual males and identify the age of each male at time of harvest.
T : Characteristics of male black-tailed deer for each treatment period from  to  harvested at Hopland, California, USA. e
number of male deer per sampling period only represents males that were used in our analyses.
Sampling period Characteristics Mean Standard deviation Range
Male harvest only
(pretreatment: –)
(𝑛 = 127)
Antler size (PC) . . . to .
Dressed weight (kg) . . . to .
Age (years) . . . to .
Male and female harvest
(treatment: –)
(𝑛 = 162)
Antler size (PC) . . . to .
Dressed weight (kg) . . . to .
Age (years) . . . to .
Male harvest only
(posttreatment: –)
(𝑛 = 161)
Antler size (PC) . . . to .
Dressed weight (kg) . . . to .
Age (years) . . . to .
year; . kg) (𝐿𝑡= 38.744(1 −𝑒−0.908(𝑡 0.062)),𝑟2= 0.18;
𝐿𝑡= 46.424(1 − 𝑒−1.225(𝑡 − 0.039)),𝑟2= 0.45,resp.).
Antler size (PC) increased slightly during the study (̂
𝑦=
0.06𝑥 − 118.93,𝑟2= 0.04,𝑃 < 0.001; Figure (b); Table ).
Overall, antler size increased with age (Figure , 𝐿𝑡= 2.82(1−
𝑒−0.4611(𝑡−2.931)),𝑟2= 0.28),andwhensizewascorrectedfor
age, the dierence in the number of deer with antlers larger
than the grand median was signicantly greater aer female
harvest began, compared with pretreatment (𝜒2= 6.32,𝑃=
0.012). When both high precipitation and low female density
occurred simultaneously, there were substantial increases in
size of antlers, such as those observed in males harvested in
 (Figures  and (b)).
Formodelsofbothantlersizeandbodyweight,extrinsic
variables incorporated into each model included an index
for population density (reected by treatment period), and
temperature and precipitation during critical periods of time
for growth of body and antlers in male deer (Tables  and ).
In our model for body size (dressed weight corrected
for age),  competing models were averaged (Table ). Two
variables were determined to be the best predictors of body
weight from % condence intervals and summed Akaike
weights (Table ). e most signicant variable was female
population density during the gestation by the mother while
themalewasin utero, but only aer female harvest began
in  (treatment and posttreatment periods combined).
e interaction between weather, specically precipitation,
andtreatmentperiodwhenthemalewasin utero was
important in predicting body size of male deer. Although
% CI overlapped zero, population density interacted with
precipitation during the rst year of growth.
We identied and averaged  competing models explain-
ing antler size (PC corrected for age) (Table ). Five variables
were inuential in determining antler size (Table ). ose
variables had a relative importance of  when Akaike weights
were summed and were dierent from  when we examined
Advances in Ecology
T : Component models for body size. Term codes: A: average
temperature during the wet season of the rst year of growth,
B: total precipitation during the wet season before harvest, C:
average temperature during the dry season before harvest, D:
average temperature during the wet season before harvest, E: total
precipitation while the male was in utero during the gestation by
the mother, F: average temperature while the male was in utero
during the gestation by the mother, G: treatment period (index for
population density) while the male was in utero during the gestation
by the mother, H: treatment period (index for population density)
during the rst year of growth, I: total precipitation during the dry
season of the rst year of growth, J: average temperature during the
dry season of the rst year of growth, K: interaction term between
precipitation and treatment during gestation by the mother, and L:
interaction term between precipitation and treatment during the dry
season of the rst year of growth. Data for these models was from
harvested black-tailed deer from Hopland, California, USA, from
 through .
Model df logLik AICc ΔWe ig ht
E, G, H, I, K, L . . . .
A, E, G, H, I, K, L . . . .
B, E, G, H, I, K, L . . . .
A, B, E, G, H, I, K, L  . . . .
B, D, E, G, H, I, K, L  . . . .
A, B, D, E, G, H, I, K, L  . . . .
C, E, G, H, I, K, L . . . .
A, C, E, G, H, I, K, L  . . . .
A, E, G, H, I, J, K, L  . . . .
A, C, D, E, G, H, I, K, L  . . . .
D, E, G, H , I, K, L . . . .
A, D, E, G, H, I, K, L  . . . .
A, B, E, G, H, I, J, K, L  . . . .
C, D, E, G, H, I, K, L  . . . .
A, E, F, G, H, I, K, L  . . . .
E, G, H, I, J, K, L . . . .
% condence intervals. e variable that most positively
inuenced antler size was body weight corrected for age.
Temperature was highly inuential during three critical peri-
ods of time. All temperature variables exhibited a negative
trendwithantlersizeofmaledeer.eperiodsinwhich
temperature was most important included, in descending
order of signicance, the dry season directly before harvest,
during the wet season during the rst year of growth, and
during the wet season the year of harvest. A negative interac-
tion occurred between precipitation and population density
(indexed by treatment period) aer , which signicantly
aected antler size. Even though the % CI overlapped
zero, female population density while the male was in utero
had a signicant positive trend in determining antler size,
but only aer female harvest began in  (treatment and
posttreatment periods combined).
4. Discussion
We demonstrated that cohort eects could have profound
inuences on body and antler size of male black-tailed deer,
Antler size (PC1)
Age (years)
024681012
14
Pretreatment
Treatment
Posttreatment
15
10
5
0
−5
F : e Von Bertalany growth curve depicts the relation
between age and antler size of male black-tailed deer during the
study. Pretreatment, treatment, and posttreatment categories repre-
sent the period when males were harvested in Hopland, California,
USA, from  to .
but climatic and density-dependent factors aected those
variables dierently. We predicted that a reduction in female
population density while the male was in utero and during
his rst year of growth would lead to increases in size of body
and antlers of male deer. Following our prediction, treatment
period, an index to female population density, was a signif-
icant predictor of body size. Specically, female population
density aer  (aer female harvest had begun—treatment
andposttreatmentcombined)waspositivelyrelatedtobody
size of male deer at time of harvest. Female density during the
rst year of growth of the male was an important predictor of
thebodysizeofmaledeerattimeofharvest.Notably,younger
( years of age) males harvested during posttreatment had
larger antlers and obtain heavier body weight than older
males (– year olds), which were reared during a time
of high female population densities (Figures  and (a)).
Intraspecic competition between females and young likely
was high early in the study, reducing the energy available for
females to invest in ospring, probably stunting the growth
of males early in life. Other studies also have documented
that cervids born small remain small as adults, even when
conditionsarefavorablelaterinlife[].
We predicted that high annual precipitation during criti-
cal periods for growth (while in utero,duringtherstyearof
growth, and during the year of harvest) would positively aect
bodyandantlersizeofmales.Contrarytoourprediction,
precipitation by itself had little inuence on either body or
antler size. Only for the antler size model did precipitation,
specically during the rst summer of life, have strong
predictive power.
Advances in Ecology
T : Component models for antler size (PC). Term codes:
A: average temperature during the wet season of the rst year of
growth, B: total precipitation during the dry season before harvest,
C: total precipitation during the wet season before harvest, D:
average temperature during the dry season before harvest, E: weight
correcte d for age (residuals), F: tota l precipitation while th e male was
in utero during the gestation by the mother, G: average temperature
while the male was in utero during the gestation by the mother,
H: treatment period (index for population density) while the male
was in utero during the gestation by the mother, I: treatment period
(index for population density) during the rst year of growth, J: total
precipitation during the dry season of the rst year of growth, K:
total precipit ation during t he wet season of the rst ye ar of growth, L:
average temperatu re during the dry sea son of the rst year of growth,
M: interaction term between precipitation and treatment during the
wetseasonoftherstyearofgrowth,N:interactiontermbetween
precipitation and treatment during the wet season of the rst year of
growth. Data for these models was from harvested black-tailed deer
from Hopland, California, USA, from  through .
Model df logLik AIC ΔWe i g ht
A, D, E, F, H, I, K . . .
A, D, E, F, J, K, L, N  . . . .
A, C, D, E, F, J, K, L, N  . . . .
A, D, E, F, H, I, J, K, L, N  . . . .
A, D, E, F, H, J, K, L, N  . . . .
A, C, D, E, F, H, I, J, K, L, N  . . . .
A, C, D, E, F, H, J, K, L, N  . . . .
A, D, E, F, J, L, N . . . .
A, D, E, F, G, H, I, K  . . . .
A, C, D, E, F, H, I, K  . . . .
A, D, E, F, H, J, L, N  . . . .
A, D, E, F, H, I, K, L  . . . .
A, C, D, E, F, H, J, L, N  . . . .
A, D, E, F, H, I, K, M  . . . .
A, D, E, F, G, J, K, L, N  . . . .
A, B, D, E, F, J, K, L, N  . . . .
A, D, E, F, H, I, J, K  . . . .
Our results, however, did arm that there was a signi-
cant interaction between precipitation and treatment period,
revealed by the -year drought during the study. Indeed,
that interaction between female population density and
precipitation had a signicant eect on both antler and body
size of males. In the body-size model, there was a negative
relation between precipitation during the gestation by the
mother and female density aer  (aer female harvest had
begun and female population density was lower). Similarly,
that interaction was signicant in the model for antler size,
but this time involving precipitation during the rst year of
growth by the male. A reasonable explanation for this result
is the increased rainfall during , which also corresponded
with some of the lowest female densities following  years
of female harvest. High precipitation alone did not result
in large antler and body sizes documented during  and
, because there was even more precipitation in  and
, yet deer were signicantly smaller during those years.
Our models oer evidence that the positive eects of female
harvest overrode negative inuences of the drought and
resulted in greater body weight and antler size, a conclusion
also reached by McCullough [].
As expected, temperature had a negative relation with
size of male deer during three critical periods of time—
while in utero, during the rst year of growth, and the year
immediately before harvest. Increases in temperature during
the wet season likely reduced snowpack at high elevations
and hereby constrained late season forage by decreasing
water availability in late spring from snowmelt. Late spring
is a critical time for deer in a Mediterranean ecosystem,
because females with young must lactate as forage senesces
[]. Unexpectedly, temperature variables only were strong
predictors of antler size but not body weight. Male deer grow
antlers from March through July [], which coincides with
some of the hottest temperatures, particularly during June
and July (Figure ). e negative relation ostensibly occurred
because of eects of hot temperatures on the senescence of
forage when antlers were still growing.
We faced several challenges in conducting this study. We
were constrained in some analyses because of issues concern-
ing missing data. First, we attempted to examine eects of
the normalized dierence vegetation index (NDVI) on body
andantlergrowthofdeer.Unfortunately,toofewimages
were available because of cloud cover or missing scenes to
make that assessment possible across years. Nevertheless,
we observed positive correlations (data not shown) between
weather variables and available NDVI scenes. During some
years, there also was incomplete data on antler measurements
of deer []. We dealt with this shortcoming by interpolating
antler metrics for no more than one measurement for  of
 deer, with few observable eects on results. Data from
 were too sparse and therefore not included in analyses.
Moreover,datawerenotcollectedannuallyonpopulation
size, so we used an index for female population density
by dividing the study into two categories—pretreatment
compared with the combined treatment (female harvest)
and posttreatment periods. Indeed, population density with
respect to carrying capacity (𝐾) is reected by female
condition and reproduction []. We have no data on how
selectivity of hunters for large males may have changed over
the course of the study, but there is no obvious reason to
believe it did.
Although female population size was not recorded during
the study, three density-dependent patterns allowed us to
infer that female density decreased suciently to allow for
improvements in physical condition during the treatment
period. First, females < years old were mistaken for older
females because of their large body size [], indicating that
population densities were below 𝐾and females were in
good nutritional condition [, , ]. Pregnancy of females
< years old also increased markedly during the treatment
period []. Cementum annuli, induced by periods of annual
stress, were more obscure aer female harvest began [].
McCullough [] noted that those results indicated a higher-
quality diet, likely leading to improvements in body condition
of females, which increased recruitment of young as well as
the size of male ospring.
Advances in Ecology
T : Model-averaged coecients for body size of black-tailed deer during – in Hopland, California, USA. Abbreviations of model
parameters are as follows: Precip: total monthly precipitation; Temp.: average monthly temperature; D: the dry season (May–September); W:
the wet season (October–April); in utero: the period during the full gestation of the mother (October–April); grow: the rst year of growth
of the male (May–April) split into wet and dry seasons; Harv: the year prior to harvest of the male (October–September) split into wet and
dry seasons; Treat: the treatment period (before: pretreatment, or aer: the combined treatment and posttreatment); Int: the interaction
term between precipitation and treatment period during gestation by the mother or the rst year of growth; Antler (PC): the rst principal
component for antler size (large antlers are negative; small antlers are positive). Bold values in the condence interval (CI) column represent
parameters that did not overlap zero.
Model parameter Estimate Std. error Adjusted SE % CI
(Intercept) . . . . to .
Precip in utero . . . . to .
Tre a t in utero [aer] 6.1000 1.9630 1.9680 0.7814 to 9.8699
Tre a t grow [aer] . . . . to .
Precip grow D . . . . to .
Int treat in utero [aer] &precip 0.0053 0.0021 0.0021 0.0094 to 0.0007
Int treat grow [aer] &precip D . . . . to .
AvTem grow W . . . . to .
Harv yr PW . . . . to .
Harv yr TW . . . . to .
Harv yr TD . . . . to .
Tem grow D . . . . to .
Tem mom gest . . . . to .
T : Model-averaged coecients for antler size of black-tailed deer during – in Hopland, California, USA. Abbreviations of
model parameters are as follows: Precip: total monthly precipitation; Temp: average monthly temperature; in utero: the period while the
male was in utero during the full gestation (October–April); D: the dry season (May–September); W: the wet season (October–April); grow:
therstyearofgrowthofthemale(MayApril)splitintowetanddryseasons;Harv:theyearpriortoharvestofthemale(OctoberSeptember)
split into wet and dry seasons; Treat: the treatment period (before: pretreatment, or aer: the combined treatment and posttreatment); Int: the
interaction term between precipitation and treatment period during the rst year of growth or while in utero; Weight: body weight (dressed
weight) corrected for age using the residuals. Bold values in the condence interval (CI) column represent parameters that did not overlap
zero.
Model parameter Estimate Std. error Adjusted SE % CI
(Intercept) . . . . to .
Tem grow W0.4199 0.1176 0.1179 0.6577 to 0.1669
Harv yr T D 0.5875 0.1661 0.1664 0.9168 to 0.2565
Harv yr T W 0.0798 0.0253 0.0254 0.1305 to 0.0289
Weight 0.1295 0.0107 0.0107 0.1080 to 0.1502
Tem in utero . . . . to .
Tre a t in utero . . . . to .
Precip grow D . . . . to .
Tre a t grow [aer] . . . . to .
Precip grow W . . . . to .
Int treat grow [aer] &precip W0.0011 0.0004 0.0004 0.0019 to 0.0003
Harv yr PW . . . . to .
Precip in utero . . . . to .
Tem grow D. . . . to .
Common conditions experienced by a cohort can result
in distinguishable dierences in phenotype when compared
with other cohorts of the same age [, , ]. erefore,
identifying the long-term eects of environmental factors
during early stages of life is critical to understanding patterns
of growth and reproduction in populations of large herbi-
vores [, ]. Unfortunately, the interaction between weather
and population density is not always well understood [].
Even on an optimal diet, ospring originally from an area
with high population density and low resource availability
exhibited suppressed growth for  generation in domestic
sheep (Ovis aries) [] and did not recover until  generations
in white-tailed deer (Odocoileus virginianus)[].Inwhite-
tailed deer, survival was directly related to the body condition
of the mother during the previous two winters []. In
contrast,otherstudieshavereportedthatdeerwereable
Advances in Ecology
to recover from periods of poor nutrition and overcome
cohort eects [–]. Consequently, understanding how a
cohort can be aected by weather or population density has
immense implications for the management and conservation
of ungulates.
We were able to construct models for predicting body and
antler size in male black-tailed deer living in a Mediterranean
ecosystem. Our research oers new insights into the inter-
action between extrinsic and intrinsic factors, and eects of
maternal and postnatal conditions on overall size of males
at time of harvest. Biologists can evaluate eects of harvest
on trends of antler size in relation to extrinsic factors during
gestation by the mother and the rst year of growth of
male deer. Even minor changes in quality and availability of
forage have resulted in relatively large uctuations in rate of
parturition and recruitment of young, because of the high
energetic requirements necessary for gestation and lactation
[,].Incervids,acohort-widedecreaseinbodymass
could have lasting consequences, aecting the reproductive
performance of a population, because of the close association
between size of males and reproductive success [, –].
We have created a valuable model for understanding and
examining eects of population density in a variable climate.
Moreover, our study provides additional information about
interactions between extrinsic and intrinsic factors in large
mammalianherbivoresanditseectsonantlersize[,,
, , , , ]. Our research is crucial for understanding
the role of environmental factors in studies on body size and
size of horn-like structures in relation to harvest of males
[, ]. In addition to considering eects of harvest on the
size of horn-like structures, our research demonstrates that
density-dependent inuences and cohort eects need to be
taken into account. Indeed, there is increasing evidence that
density-dependent mechanisms may override benecial and
detrimental eects of climate [, , ].
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
Acknowledgments
e authors thank many individuals that helped collect data
during the initial study and were already acknowledged in
McCullough (). For this study, the authors thank Z.
Simpson for data processing and D. M. Walcott and K. M.
Stewart for helpful reviews of their paper.
References
[] B.-E. Sæther, “Environmental stochasticity and population
dynamics of large herbivores: a search for mechanisms,Tre nds
in Ecology and Evolution,vol.,no.,pp.,.
[] M. D. Pitt and H. F. Heady, “Responses of annual vegetation
to temperature and rainfall patterns in Northern California,
Ecology,vol.,no.,pp.,.
[] K.L.Monteith,L.E.Schmitz,J.A.Jenks,J.A.Delger,andR.
T. Bowyer, “Growth of male white-tailed deer: consequences of
maternal eects,Journal of Mammalogy,vol.,no.,pp.
, .
[] J. O. Ogutu and N. Owen-Smith, “ENSO, rainfall and tempera-
ture inuences on extreme population declines among African
savanna ungulates,Ecology Letters,vol.,no.,pp.,
.
[] S. Bø and O. Hjeljord, “Do continental moose ranges improve
during cloudy summers?” Canadian Journal of Zoology,vol.,
no.,pp.,.
[] R. Langvatn, S. D. Albon, T. Burkey, and T. H. Clutton-
Brock, “Climate, plant phenology and variation in age of rst
reproduction in a temperate herbivore,Journal of Animal
Ecology,vol.,no.,pp.,.
[] I. Herndal, B.-E. Sæther, E. J. Solberg, R. Andersen, and K. A.
Høgda, “Population characteristics predict responses in moose
bodymasstotemporalvariationintheenvironment,Journal
of Animal Ecology,vol.,no.,pp.,.
[] E.J.Solberg,B.-E.Saether,O.Strand,andA.Loison,“Dynamics
of a harvested moose population in a variable environment,
Journal of Animal Ecology, vol. , no. , pp. –, .
[] R. Aanes, B.-E. Sæther, and N. A. Øritsland, “Fluctuations of
an introduced population of Svalbard reindeer: the eects of
density dependence and climatic variation,Ecography,vol.,
no. , pp. –, .
[] J. G. Kie, R. T. Bowyer, and K. M. Stewart, “Ungulates in western
coniferous forests: habitat relationships, population dynamics,
and ecosystem processes,” in Mammal Community Dynamics:
Management and Conservation in the Coniferous Forests of
Western North America,C.J.ZabelandR.G.Anthony,Eds.,pp.
–, Cambridge University Press, Cambridge, UK, .
[] K. M. Stewart, R. T. Bowyer, B. L. Dick, B. K. Johnson, and J.
G. Kie, “Density-dependent eects on physical condition and
reproduction in North American elk: an experimental test,
Oecologia,vol.,no.,pp.,.
[] T. H. Clutton-Brock, M. Major, and F. E. Guinness, “Population
regulation in male and female red deer,e Journal of Animal
Ecology,vol.,no.,pp.,.
[] H. D. Picton, “Climate and the prediction of reproduction of
threeungulatespecies,Journal of Applied Ecology,vol.,no.,
pp. –, .
[] E. Post, N. C. Stenseth, I. Rolf, and J.-M. Fromentin, “Global
climate change and phenotypic variation among red deer
cohorts,ProceedingsoftheRoyalSocietyofLondonB:Biological
Sciences,vol.,no.,pp.,.
[] E. Post and N. C. Stenseth, “Climatic variability, plant phenol-
ogy, and northern ungulates,Ecology,vol.,no.,pp.
, .
[] M. C. Forchhammer, T. H. Clutton-Brock, J. Lindstr¨
om, and S.
D. Albon, “Climate and population density induce long-term
cohort variation in a northern ungulate,Journal of Animal
Ecology,vol.,no.,pp.,.
[] E. Richard, S. E. Simpson, S. A. Medill, and P. D. Mcloughlin,
“Interacting eects of age, density, and weather on survival
and current reproduction for a large mammal,Ecology and
Evolution,vol.,no.,pp.,.
[] J. Bernardo, “Maternal eects in animal ecology,” American
Zoologist,vol.,no.,pp.,.
[] M. Festa-Bianchet and J. T. Jorgenson, “Selsh mothers: repro-
ductive expenditure and resource availability in bighorn ewes,
Behavioral Ecology,vol.,no.,pp.,.
 Advances in Ecology
[] S. D. Albon, T. H. Clutton-Brock, and F. L. Guinness, “Early
development and population dynamics in red deer. II. Density-
independent eects and cohort variation,JournalofAnimal
Ecology,vol.,no.,pp.,.
[] J. M. Gaillard, D. Delorme, and J. M. Jullien, “Eects of cohort,
sex, and birth date on body development of roe deer (Capreolus
capreolus)fawns,Oecologia,vol.,no.,pp.,.
[] M. A. Keech, R. D. Boertje, R. T. Bowyer, and B. W. Dale,
“Eects of birth weight on growth of young moose: do low-
weight neonates compensate?” Alces,vol.,pp.,.
[] M.A.Keech,R.T.Bowyer,J.M.VerHoef,R.D.Boertje,B.
W. Dale, and T. R. Stephenson, “Life-history consequences of
maternalconditioninAlaskanmoose,e Journal of Wildlife
Management,vol.,no.,pp.,.
[] S. D. Cˆ
ot´
e and M. Festa-Bianchet, “Birthdate, mass and survival
in mountain goat kids: eects of maternal characteristics and
forage quality,Oecologia,vol.,no.,pp.,.
[] K. T. Schmidt, A. Stien, S. D. Albon, and F. E. Guinness, “Antler
length of yearling red deer is determined by population density,
weather and early life-history,Oecologia,vol.,no.,pp.
, .
[] E. D. Freeman, R. T. Larsen, K. Clegg, and B. R. McMillan,
“Long-lasting eects of maternal condition in free-ranging
cervids,PLoS ONE,vol.,no.,ArticleIDe,.
[] S. Hamel, J.-M. Gaillard, M. Festa-Bianchet, and S. D. Cˆ
ot´
e,
“Individual quality, early-life conditions, and reproductive suc-
cess in contrasted populations of large herbivores,Ecology,vol.
,no.,pp.,.
[]L.D.Mech,M.E.Nelson,andR.E.McRoberts,“Eects
of maternal and grandmaternal nutrition on deer mass and
vulnerability to wolf predation,Journal of Mammalogy,vol.,
no. , pp. –, .
[] M.N.Clements,T.H.Clutton-Brock,S.D.Albon,J.M.Pember-
ton, and L. E. B. Kruuk, “Getting the timing right: antler growth
phenology and sexual selection in a wild red deer population,
Oecologia,vol.,no.,pp.,.
[]R.M.Sibly,W.Zuo,A.Kodric-Brown,andJ.H.Brown,
Renschsruleinlargeherbivorousmammalsderivedfrom
metabolic scaling,American Naturalist,vol.,no.,pp.
, .
[] D. R. McCullough, “Male harvest in relation to female removals
in a black-tailed deer population,e Journal of Wildlife
Management,vol.,no.,pp.,.
[] A. H. Murphy and M. F. Heady, “Vascular plants of the Hop-
land Field Station, Mendocino County, California,” Wasman n
Journal of Biology,vol.,no.-,pp.,.
[] M.M.Conner,M.M.Jaeger,T.J.Weller,andD.R.McCullough,
“Eect of coyote removal on sheep depredation in northern
California,Journal of Wildlife Management,vol.,no.,pp.
–, .
[] R. C. Team, R: A Language and Environment for Statistical Com-
puting. R Foundation for Statistical Computing, Vienna, Austria,
2012, Foundation for Statistical Computing, Vienna, Austria,
.
[] K. Aho, Asbio: A Collection of Statistical Tools for Biologists,
CRAN, .
[] R. D. Cameron and D. B. Stone, “To weigh or to mass? e
question is in the balance,Wildlife Society Bulletin,vol.,no.
, p. , .
[] A. E. Anderson, D. E. Medin, and D. C. Bowden, “Growth and
morphometry of the carcass, selected bones, organs, and glands
of mule deer,Wildlife Monographs,no.,pp.,.
[] D. R. McCullough and P. Beier, “Upper vs. Lower molars for
cementum annuli age determination of deer,e Journal of
Wildlife Management,vol.,no.,pp.,.
[] D. C. omas and P. J. Bandy, “Accuracy of dental-wear age esti-
mates of black-tailed deer,e Journal of Wildlife Management,
vol.,no.,pp.,.
[] D. P. Swain, “A problem with the use of meristic characters to
estimate developmental stability,” e American Naturalist,vol.
, no. , pp. –, .
[] K. M. Stewart, R. T. Bowyer, J. G. Kie, and W. C. Gasaway,
Antler size relative to body mass in moose: tradeos associated
with reproduction,Alces, vol. , pp. –, .
[] R.T.Bowyer,K.M.Stewart,J.G.Kie,andW.C.Gasaway,“Flu-
ctuating asymmetry in antlers of Alaskan moose: size matters,
Journal of Mammalogy,vol.,no.,pp.,.
[] R. J. Goss, Deer Antlers, Regeneration, Evolution and Function,
Academic Press, New York, NY, USA, .
[] C. Vanp´
e, J.-M. Gaillard, P. Kjellander et al., “Antler size provides
an honest signal of male phenotypic quality in roe deer,e
American Naturalist,vol.,no.,pp.,.
[] G. A. Nelson, Fishmethods: Fishery Science Methods and Models
in R,CRAN,.
[] A. J. Fabens, “Properties and tting of the von bertalany
growth curve,Growth, Development and Aging,vol.,no.,
pp. –, .
[] E.M.Zullinger,R.E.Ricklefs,K.H.Redford,andG.M.Mace,
“Fitting sigmoidal equations to mammalian growth curves,
Journal of Mammalogy,vol.,no.,pp.,.
[] H. Walter and E. Box, “Global classication of natural terrestrial
ecosystems,Ve getat i o ,vol.,no.,pp.,.
[] P.J.Pekins,K.S.Smith,andW.W.Mautz,“eenergycostof
gestationinwhite-taileddeer,Canadian Journal of Zoology,vol.
, no. , pp. –, .
[] R. T. Bowyer, “Timing of parturition and lactation in southern
mule deer,Journal of Mammalogy,vol.,no.,pp.,
.
[] J. Fox and S. Weisberg, An R Companion to Applied Regression,
Sage, ousand Oaks, Calif, USA, .
[] K. Aho, Foundational and Applied Statistics for Biologists Using
R, CRC Press, Boca Raton, Fla, USA, .
[] K. Barton, MuMIn: Multi-Model Inference,CRAN,.
[] K. P. Burnham and D. R. Anderson, Model Selection and Mul-
timodel Inference: A Practical Information-eoretic Approach,
Springer Science & Business Media, New York, NY, USA, nd
edition, .
[] T. W. Arnold, “Uninformative parameters and model selection
using akaike’s information criterion,e Journal of Wildlife
Management,vol.,no.,pp.,.
[] W. J. Conover, Practical Nonparametric Statistics,TexasTech
University, Lubbock, Tex, USA; John Wiley & Sons, New York,
NY, USA, nd edition, .
[] R. F. Dasmann and R. D. Taber, “Behavior of Columbian black-
tailed deer with reference to population ecology,Journal of
Mammalogy,vol.,no.,pp.,.
[] R. T. Bowyer, C. B. Vernon, M. K. Stewart, C. J. Whiting, and
L. K. Monteith, “Density dependence in ungulates: a review of
causes, and concepts with some clarications,California Fish
and Game,vol.,no.,pp.,.
[] B. M. Pierce, V. C. Bleich, K. L. Monteith, and R. T. Bowyer,
“Top-down versus bottom-up forcing: evidence from mountain
Advances in Ecology 
lions and mule deer,Journal of Mammalogy,vol.,no.,pp.
–, .
[] K. L. Monteith, V. C. Bleich, T. R. Stephenson et al.,“Life-history
characteristics of mule deer: eects of nutrition in a variable
environment,Wildlife Monograph s,vol.,no.,pp.,
.
[] D. R. McCullough, “Failure of the tooth cementum aging tech-
nique with reduced population density of deer,Wildlife Society
Bulletin,vol.,no.,pp.,.
[] A.Beckerman,T.G.Benton,E.Ranta,V.Kaitala,andP.Lund-
berg, “Population dynamic consequences of delayed life-history
eects,Trends in Ecology & Evolution,vol.,no.,pp.,
.
[] M. Douhard, J.-M. Gaillard, D. Delorme et al., “Variation in
adult body mass of roe deer: early environmental conditions
inuence early and late body growth of females, Ecology,vol.
,no.,pp.,.
[] R. Mobæk, A. Mysterud, Ø. Holand, and G. Austrheim, “Tem-
poral variation in density dependent body growth of a large
herbivore,Oikos,vol.,no.,pp.,.
[]L.D.Mech,R.E.McRoberts,R.O.Peterson,andR.E.
Page, “Relationship of deer and moose populations to previous
winters’ snow,eJournalofAnimalEcology,vol.,no.,pp.
–, .
[] B. K. Strickland, S. Demarais, and P. D. Gerard, “Variation in
mass and lactation among cohorts of white-tailed deer Odo-
coileus virginianus,” Wildlife Biology,vol.,no.,pp.,
.
[] K. L. Gee, S. L. Webb, and P. D. Jones, “Age-specic changes in
body mass and delayed physical development of a known-aged
sample of wild, white-tailed deer,” Wildlife Biology in Practice,
vol.,no.,pp.,.
[] D. M. Wolcott, J. Asmus, and F. W. Weckerly, “Overcoming
cohort eects in a Mediterranean ecosystem: the role of density
and precipitation on southern mule deer body mass,e Jour-
nal of Wildlife Management,vol.,no.,pp.,.
[] R. G. White, “Foraging patterns and their multiplier eects on
productivity of northern ungulates,Oikos,vol.,no.,pp.
–, .
[] E.A.Lenart,R.T.Bowyer,J.VerHoef,andR.W.Ruess,“Climate
change and caribou: eects of summer weather on forage,
Canadian Journal of Zoology, vol. , no. , pp. –, .
[] R.C.Cook,D.L.Murray,J.G.Cook,P.Zager,andS.L.Monfort,
“Nutritional inuences on breeding dynamics in elk,Canadian
Journal of Zoology,vol.,no.,pp.,.
[] K.L.Monteith,T.R.Stephenson,V.C.Bleich,M.M.Conner,
B. M. Pierce, and R. T. Bowyer, “Risk-sensitive allocation in
seasonal dynamics of fat and protein reserves in a long-lived
mammal,JournalofAnimalEcology,vol.,no.,pp.,
.
[] R. T. Bowyer, “Antler characteristics as related to social status of
male southern mule deer,e Southwestern Naturalist,vol.,
no. , pp. –, .
[] T. H. Clutton-Brock, M. Major, S. D. Albon, and F. E. Guinness,
“Early development and population dynamics in red deer. I.
Density-dependent eects on juvenile survival,e Journal of
Animal Ecology,vol.,no.,pp.,.
[] E. J. Solberg, A. Loison, J.-M. Gaillard, and M. Heim, “Lasting
eects of conditions at birth on moose body mass,Ecography,
vol.,no.,pp.,.
[] C. Azorit, M. Analla, R. Carrasco, and J. Mu˜
noz-Cobo, “Inu-
ence of age and environment on antler traits in Spanish red deer
(Cervus elaphus hispanicus),Zeitschri f¨
ur Jagdwissenscha,
vol.,no.,pp.,.
[] R. B. Weladji, Ø. Holand, G. Steinheim, J. E. Colman, H.
Gjøstein, and A. Kosmo, “Sexual dimorphism and intercorhort
variationinreindeercalfantlerlengthisassociatedwithdensity
and weather,Oecologia,vol.,no.,pp.,.
[] M. Festa-Bianchet and R. Lee, “Guns, sheep and genes: when
and why trophy hunting may be a selective pressure,” in Recre-
ational Hunting, Conservation and Rural Livelihoods: Science
and Practice, pp. –, Wiley-Blackwell, Oxford, UK, .
[]K.L.Monteith,R.A.Long,V.C.Bleich,J.R.Heelnger,P.
R. Krausman, and R. T. Bowyer, “Eects of harvest, culture, and
climate on trends in size of horn-like structures in trophy ungu-
lates,Wil dlife Monographs,vol.,no.,pp.,.
... Population density can influence habitat quality and, thereby, antler growth and absolute size for deer (Thalmann et al. 2015). We included antlered deer harvest as a relative index of antlered deer in the area, which is a better proxy for abundance than antlered and antlerless deer combined (Snow et al. 2015). ...
... Habitat quality influences a variety of demographic and behavioral characteristics in cervids, including survival and recruitment (Ginnett andYoung 2000, Hurley et al. 2014), body mass and condition (Pettorelli et al. 2002), and timing of seasonal migration (Mysterud et al. 2017). Antler growth and absolute size have been positively associated with environmental conditions and habitat quality in several ungulates, including roe deer (Capreolus capreolus; Ramanzin and Sturaro 2014), red deer (Cervus elaphus; Mysterud et al. 2005), white-tailed deer (Jones et al. 2010, Edwards 2012, and black-tailed deer (Odocoileus hemionus columbianus; Thalmann et al. 2015). Environmental conditions that reduce habitat quality while in utero and early life can have lasting effects on body condition and antler size in males as adults (Monteith et al. 2009, Thalmann et al. 2015. ...
... Antler growth and absolute size have been positively associated with environmental conditions and habitat quality in several ungulates, including roe deer (Capreolus capreolus; Ramanzin and Sturaro 2014), red deer (Cervus elaphus; Mysterud et al. 2005), white-tailed deer (Jones et al. 2010, Edwards 2012, and black-tailed deer (Odocoileus hemionus columbianus; Thalmann et al. 2015). Environmental conditions that reduce habitat quality while in utero and early life can have lasting effects on body condition and antler size in males as adults (Monteith et al. 2009, Thalmann et al. 2015. ...
Article
Full-text available
For more than a century, the Boone and Crockett Club has kept records of harvest locations for 38 categories of North American big game, which includes measurement details and sizes of enduring characteristics (e.g., antlers, skulls, horns). All white‐tailed deer (Odocoileus virginianus) entered in the Club records (i.e., record deer) have large antlers because the measurements must meet or exceed a minimum size to qualify for entry. We used the records from the Club to test the hypothesis that the spatial distribution in the harvest of record deer was related to the number of antlered deer harvested, variations in land productivity, soil mineral content, and attributes of land cover for 9 states in the midwestern United States. We used a Bayesian theoretical framework to develop a spatial model that examined the influence of explanatory variables on the number of record deer harvested in each county. In our study area, 3,658 record deer were harvested in 692 of the 856 counties (80.8%). More record deer were harvested in counties that had more high‐contrast edges, less contiguous land cover, and greater variation in soil productivity. These results provide information for managers and hunters to better understand the spatial distribution of record deer and factors that are correlated with their distribution. © 2019 The Wildlife Society. Our results confirm that environmental factors known to confer habitat suitability on local geographic scales are also correlated with the distribution of record deer across large landscapes at the extent of continental regions. Specifically, these findings suggest that landscape configuration was associated with the spatial distribution of record deer across the Midwest during the study period.
... That approximates a log-likelihood ratio chi-square score of 2 with 1 degree of freedom and α = 0.15. Therefore, 85% confidence limits (1 − 0.15 = 0.85) about the coefficients of potential regression covariates coincide with the AIC selection process and can be used to identify covariates that are likely uninformative but were not excluded by the AIC criteria used during model selection [45,46]. Consequently, if the 85% confidence intervals for any coefficient of a covariate encompassed 0, we did not consider that covariate to be an important predictor. ...
... Measures of physical condition of moose are extremely useful in determining where the population was in relation to K, without having to measure K directly [1,4,5]. Nonetheless, the use of weights of young or yearlings may be biased because of potential cohort or maternal effects whereby improvements in habitat quality are not immediately reflected in the size of adults or their young [12,46,56]. A suite of life-history and population characteristics are available to help judge the relationship of an ungulate population to K but are most useful when a variety of characteristics are collected from females [1,4,5]. ...
Article
Full-text available
We analyzed harvest data to test hypotheses that nearly 4 decades of effort to reduce abundance of brown bears (Ursus arctos), black bears (U. americanus) and gray wolves (Canis lupis) in an 60,542 km2 area in south-central Alaska (Game Management Unit [GMU] 13) was positively correlated with moose (Alces alces) harvests in some time-lagged fashion. Predator-reduction efforts were progressively more aggressive over decades (both de facto and officially designated predator control) and did not have clear starting points which complicated our post hoc analyses. We documented no positive correlations (p > 0.05) between harvests of brown and black bears and subsequent moose harvests for any time lag. Moose harvest was negatively correlated with the previous years’ wolf harvest, but the relationship was weak (correlation = −0.33, p < 0.05). Consequently, we reject our hypotheses that harvest of predators was positively correlated with moose harvests. We also observed no differences in mean moose harvests during periods of officially designated wolf control (2005–2020) and a previous period (p > 0.50). We recommend that predator reductions designed to improve hunter harvests of moose be conducted within a research framework that will permit improved interpretations of results and the implementation of an adaptive-management approach to achieve management objectives.
... Arnold (2010) provided guidance that use of 85% confidence intervals is most appropriate for model selection using information-theoretic approaches, specifically AIC model selection. Therefore, confidence intervals were set at 85% to more closely align with information-theoretic approaches, in contrast to 95% confidence intervals that are more appropriate to frequentist approaches (Arnold 2010, Thalmann et al. 2015. We followed this guidance and used 85% CIs when presenting our results. ...
... The uncertainty, when combined with the parameter estimate itself, should provide guidance on interpretation of the effect. For example, a parameter estimate with a small effect (i.e., small and centered near 0) with a CI that broadly overlaps 0 (e.g.,β = 0.005, 85% CI = −0.512 to 0.986) is likely an uninformative or poorly estimated effect (Arnold 2010, Thalmann et al. 2015. A parameter estimate that is strongly negative or positive and has a CI that does not overlap 0 is strongly informative (e.g.,β = 1.13, v www.esajournals.org ...
Article
Full-text available
Variation among demographic rates for a population reflects the allocation of available energy by individuals to competing life‐history strategies. Species exhibiting slow‐paced life histories often prioritize energy allocation to adult survival over any single reproductive event, therefore maximizing future reproductive potential. Survival of adult female ungulates is generally high with little variability, whereas survival of young is lower and often highly variable. When adult survival is high with low variability, juvenile survival may have a proportionally greater effect on population growth or decline. Weather also may affect population dynamics directly by influencing survival of young or adults, or indirectly through changes in nutritional condition of adult females that influence population growth rates. We experimentally manipulated forage availability during winter, by supplementing native forage with high‐energy pelleted feed ad libitum, to a subset of a population of mule deer (Odocoileus hemionus) to understand the effects of winter nutrition on survival of adult females and their young born the subsequent summer. We evaluated the effects of winter nutrition, individual‐based parameters, and environmental covariates on survival of adult female mule deer from 2013 to 2018, and neonatal mule deer from 2014 to 2016. We documented a 26% decrease in annual survival of adult female mule deer in 2017 in response to increased snowpack during the preceding winter. Neonates born to females that receive enhanced nutrition during winter preceding parturition had higher survival to weaning (0.49, SE = 0.12), compared to neonates born to females that did not receive enhanced nutrition (0.29, SE = 0.07). We observed no effect of enhanced winter nutrition on survival of adult females. Our results suggested winter nutrition of maternal females may influence juvenile survival and demonstrates the importance of forage quality available to adult females during mid‐pregnancy. Although we were unable to detect an effect of winter forage on survival of adults, direct effects of deep winter snow resulted in lower survival of adult females. Low survival of adult females in our study population is indicative of a declining population.
... Conversely, hot temperatures during the antler-growing season can cause early forage senescence, which is shown to have a negative relationship on antler growth of black-tailed deer (O. hemionus columbianus; Thalmann et al. 2015). ...
... Effects of environmental stress may be acute or persist for many years and generations (i.e., lag theory or cohort effect; Anderson and Medin 1969, Monteith et al. 2009, Thalmann et al. 2015. Adverse environmental conditions can affect maternal health and consequently investments in offspring Jorgenson 1998, Freeman et al. 2013). ...
Article
Antler size and morphology is the resultant combination of deer age, nutrition, and genetics. Additionally, extreme environmental conditions can affect deer health, which may influence current and future antler metrics. Throughout the antler development season of 2012, the Nebraska, USA, white‐tailed deer (Odocoileus virginianus) herd experienced a combination of extreme environmental conditions including extreme drought and disease, which we hypothesized would negatively affect antler size and morphology. Our objectives were to evaluate whether 1) age‐specific antler metrics differed between deer stressed by an extreme environmental condition year compared with nonextreme condition years, and 2) subsequent age‐specific antler metrics of a cohort born during an extreme environmental condition year differed from those born during nonextreme condition years. We measured antler metrics on harvested white‐tailed deer from central Nebraska for an 8‐year period (2009–2016) that spanned the extreme environmental condition year. Over this same time period, we measured pedicle seal depth on naturally cast antlers. Some trends were apparent for specific antler metrics in particular age groups; but, overall, antler metrics measured from harvested deer were not consistently affected during the extreme environmental condition year. Conversely, pedicle seal depths responded to environmental stressors and were smaller during the extreme environmental condition year compared with nonextreme condition years. We found effects to persist for years following extreme environmental stress—antler metrics of the extreme environmental condition cohort were smaller compared with nonextreme years. These results suggest that stressors caused by extreme environmental conditions can affect deer health and be indexed using sensitive metrics taken on cast antlers. Furthermore, effects on antler metrics can persist, affecting subsequent antler expression for cohorts that experience extreme environmental conditions during their first year of life. © 2019 The Wildlife Society.
... We considered all available covariates for both time periods to directly compare the potential change in parameter estimates and their associated effect sizes on resource selection by mule deer before and after the introduction of elk. Additionally, we used 85% confidence intervals to assess the statistical significance of individual parameter estimates for standardized model coefficients to account for uncertainty in parameter estimation (Arnold 2010;Thalmann et al. 2015;Aho et al. 2017). We used the model results from resource selection functions to create maps of predicted mule deer habitat before and after elk introduction by first calculating a continuous raster surface for each model by solving the logistic equation: w(x) = exp( + + ⋯ + ) using the raster calculator in ArcGIS (v.10.3.1). ...
Article
Full-text available
Translocation of animals for conservation has a long history of successes and failures since humans began intervening with species distributions in the early part of the 20th century. Effects of translocations on other species occupying the area of introduction are rarely considered in species management plans. We hypothesized that the introduction of a large-bodied ungulate, Rocky Mountain elk (Cervus canadensis nelsoni), would cause a shift in the spatial distribution and winter habitat selection of a smaller-bodied ungulate, Rocky Mountain mule deer (Odocoileus hemionus hemionus), already occupying the study area in northeastern Nevada. We examined mule deer survey locations during an 8-year time interval from 1993 to 2001 to test hypotheses related to potential competition with or displacement of mule deer following introduction of elk in 1997. We used geospatial statistics to quantify changes in seasonal distributions of mule deer and a resource selection function (RSF) framework to model changes in selection of resources by mule deer before and after elk were translocated into our study area. Our results indicated that mule deer exhibited a shift in their core distribution by approximately 5.72 km after the introduction of elk. Mule deer changed their use of habitat by selecting shallower slopes, more north-facing aspects, and areas farther from the elk release site, where most of the elk congregated. Mule deer selected habitats with more pinyon-juniper tree cover and mixed shrublands prior to the elk translocation, indicating a potential tradeoff in thermal cover and forage quality. This research is one of the few empirical studies to describe competitive interactions between elk and mule deer on a shared winter range in North America. Implications of this research have importance for managers concerned with restoring communities to their native conditions especially where the potential for competition with non-indigenous species or other competing species with similar niche requirements exists.
... Size or performance of animals can be reflective of conditions during the year they were born (i.e., cohort effects) and are a population-level example of cross-generational effects of maternal nutrition [64][65][66] that can yield disparities in reproduction and survival advantages between large-and small-bodied animals [19,20,31,36]. Adult body mass is underpinned by maternal and grandmaternal effects [67], where the nutritional status of the mother during gestation can determine the lifetime trajectory of growth for an animal, regardless of nutritional experiences thereafter [68][69][70][71]. ...
Article
Full-text available
Nutrition underpins survival and reproduction in animal populations; reliable nutritional biomarkers are therefore requisites to understanding environmental drivers of population dynamics. Biomarkers vary in scope of inference and sensitivity, making it important to know what and when to measure to properly quantify biological responses. We evaluated the repeatability of three nutritional biomarkers in a large, iteroparous mammal to evaluate the level of intrinsic and extrinsic contributions to those traits. During a long-term, individual-based study in a highly variable environment, we measured body fat, body mass, and lean mass of mule deer (Odocoileus hemionus) each autumn and spring. Lean mass was the most repeatable biomarker (0.72 autumn; 0.61 spring), followed by body mass (0.64 autumn; 0.53 spring), and then body fat (0.22 autumn; 0.01 spring). High repeatability in body and lean mass likely reflects primary structural composition, which is conserved across seasons. Low repeatability of body fat supports that it is the primary labile source of energy that is largely a product of environmental contributions of the previous season. Based on the disparate levels in repeatability among nutritional biomarkers, we contend that body and lean mass are better indicators of nutritional legacies (e.g., maternal effects), whereas body fat is a direct and sensitive reflection of recent nutritional gains and losses.
... Mammalian growth is typically asymptotic, thereby making sigmoid growth functions more realistic (Leberg et al. 1989). Popular growth models include the Von Bertalanffy asymptotic growth, Logistic, and Gompertz models (Zullinger et al. 1984;Lesage et al. 2001;Canaza-Cayo et al. 2015;Thalmann et al. 2015; Table 1). There are three growth curve parameters for each of these curves: A, the asymptotic mature body size or antler size; B, the parameter that influences the proportion of asymptotic size achieved at birth; and k, the parameter that influences the maturation rate or how fast individuals approach maximum size. ...
Article
Full-text available
Body size and secondary sexual characteristics are drivers of male reproductive success among polygynous species. A gene complex found to be associated with morphology in several species is the major histocompatibility complex (MHC). However, while several studies have found that greater MHC diversity is associated with larger body size and secondary sexual characteristics, other studies have demonstrated that maximal MHC diversity is not always optimal for the individual’s fitness. This study tested if MHC diversity, measured as pairwise allelic distances at each of two unlinked MHC II loci (exon 2 for the classical antigen-binding protein MHC-DRB and exon 2 for the accessory protein MHC-DOB), was associated with body size (male and female) or antler size in a semi-wild enclosed population of white-tailed deer (Odocoileus virginianus). After accounting for the effect of age on body and antler size, we used residual analysis to assess whether MHC allelic distances explained any of the remaining variation in body and antler size. While we found no associations between physical characteristics and MHC-DRB, we found that both male body and antler size were associated with MHC-DOB nucleotide allelic distances. Specifically, we found a quadratic relationship between MHC-DOB and male body size, where body size peaked at moderate MHC-DOB nucleotide allelic distance. However, we found a positive linear association between MHC-DOB nucleotide allelic distances and antler size. Neither MHC-DRB nor MHC-DOB influenced female body size, even though the average allelic distances of males and females were not significantly different. Our results suggest that MHC-DOB, or a gene genetically linked to this locus, may influence male morphological characteristics in white-tailed deer.
... At sufficiently high numbers relative to K, density dependence may outweigh even densityindependent events that might be beneficial (Stewart et al. 2005). Conversely, severe weather, such as extreme drought, may be overridden by effects of increased nutrition related to reduced population numbers (Thalmann et al. 2015). Furthermore, body mass of reindeer (Rangifer tarandus) was more important than spring phenology in determining production of young in a severe arctic environment, largely because of carryover effects from reserves accumulated in previous seasons (Veiberg et al. 2016). ...
Article
Full-text available
Biologists often must use incomplete information to make recommendations concerning harvest of large mammals. Consequently, those recommendations must draw on a firm understanding of the ecology of the species in question, along with selection of the most applicable population characteristics on which to base harvest-both essential components for prudent management. Density-dependent processes, which are ubiquitous among populations of large mammals, may be counterintuitive because of unexpected patterns in recruitment coincident with changes in population size. Misconceptions concerning population dynamics of ungulates also can occur when demo-graphics are based solely on correlations with environmental factors. Further, the concept of a harvestable surplus can be misleading for managing ungulate populations, because of the parabolic relationship between population size and number of recruits-harvest determines the surplus rather than vice versa. Understanding consequences of mortality, especially relative components of compensatory or additive mortality, also is necessary. Knowledge of the proximity of an ungulate population to ecological carrying capacity (K) is required to fully assess whether most mortality is compensatory or additive. We describe selected life-history traits and population characteristics of ungulates useful in parametrizing where populations are in relation to K, thereby allowing for a reasonable harvest despite some uncertainty in population size. We advocate an adaptive-management approach while monitoring those life-history traits to evaluate the suitability of a particular harvest strategy. ALCES VOL. 56: 15-38 (2020)
... Maternal condition can affect birth weight and offspring growth rate (Verme 1963, Cothran et al. 1983, Loudon et al. 1983, Skogland 1984, and such maternal effects may persist into adulthood (Mech et al. 1991, Schultz and Johnson 1995, Monteith et al. 2009, Freeman et al. 2013, Thalmann et al. 2015. We consider the effects of winter NAO t-2 on antler mass as a possible maternal effect stemming from conditions during gestation (or post-parturition; see below). ...
Article
Full-text available
Research in northern latitudes confirms that climate teleconnections exert important influences on ungulate fitness, but studies from regions with milder climates are lacking. We explored the influence of the Pacific Decadal Oscillation (PDO), Northern Atlantic Oscillation (NAO), and El Niño-Southern Oscillation (ENSO) on male, 2.5-year-old white-tailed deer (Odocoileus virginianus) antler and body mass in Mississippi, USA, a region with mild winters and warm, humid summers. Explanatory variables were seasonal averages of each climate index extending back to 3 years prior to account for possible maternal and lag effects. Seasonal climate indices from the period of gestation and the first year of life were correlated with deer morphometrics. Reduced antler mass was largely correlated (R2 = 0.52) with PDO values indicating dry conditions during parturition and neonatal development and NAO values indicating warmer than normal winters during gestation and the first year of life. Body mass was less correlated (R2 = 0.16) to climate indices, responding negatively to warmer winter weather during the first winter of life. Climate may promote variable fitness among cohorts through long-term effects on male competition for dominance and breeding access. Because broad-scale climate indices simplify complex weather systems, they may benefit management at larger scales. Although this study compared climate with morphological variables, it is likely that demographic characteristics can likewise be modeled using climate indices. As climate change in this region is projected to include greater variability in summer precipitation, we may see concomitantly greater variability in fitness among cohorts of white-tailed deer.
... Estimates of regression coefficients of age (b 1 ¼ 5:49 and 4.36) and fielddressed weight (b 2 ¼ 0:88 and 0.75) in Eq 1 showed that there were positive linear relationships between the effects of age and field-dressed weight and the traits of antler beam diameter and length. This result is consistent with previous reports [6,7,43]. Our results also showed that age is related to antler characteristics and body size in WTD. ...
Article
Full-text available
Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg-Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) learning algorithms, to evaluate the relative accuracy in predicting antler beam diameter and length using age and dressed body weight in white-tailed deer. Data utilized for this study were obtained from male animals harvested by hunters between 1977-2009 at the Berry College Wildlife Management Area. Metrics for evaluating model performance indicated that linear and ANN models resulted in close match and good agreement between predicted and observed values and thus good performance for all models. However, metrics values of Mean Absolute Error and Root Mean Squared Error for linear model and the ANN-BR model indicated smaller error and lower deviation relative to the mean values of antler beam diameter and length in comparison to other ANN models, demonstrating better agreement of the predicted and observed values of antler beam diameter and length. ANN-SCG model resulted in the highest error within the models. Overall, metrics for evaluating model performance from the ANN model with BR learning algorithm and linear model indicated better agreement of the predicted and observed values of antler beam diameter and length. Results of this study suggest the use of ANN generated results that are comparable to Linear Models of harvest data to aid in the development of strategies to manage white-tailed deer.
Article
Full-text available
We used 13 years of historical data to investigate effects of coyote (Canis latrans) removal on depredation of domestic sheep. The 2,168-ha study area maintained >1,000 breeding ewes that produced lambs yearly. Records from 1981 through 1994, which included numbers of sheep, numbers of sheep known killed by coyotes, known numbers of coyotes removed, and annual numbers of trapper hours were summarized and analyzed on a yearly, seasonal, and monthly basis. We used regression analysis and found that annual, seasonal, or monthly depredation losses were not correlated with number of coyotes removed. Both annual number of lambs killed and number of coyotes removed were positively correlated with number of trapper hours. We used a cross-correlation analysis to detect any relation between coyote removal and subsequent depredation losses at all monthly intervals from 0 to 24 months. We found a trend of low negative correlation between depredation losses and number of coyotes removed for lags of 2-12 months, suggesting some reduction of sheep killing due to control efforts. Low correlations within years may be due to inconsistent removal of depredating coyotes while removing primarily young, nondepredating coyotes. Lack of correlation between years may have occurred because past control efforts have not had a lasting reduction on coyote density due to immigration, the compensatory nature of control efforts on coyote mortality, reproductive compensation in the resident coyote population, or all 3 factors.
Chapter
Full-text available
Conservation of mammals in the coniferous forests of western North America has shifted in recent years from species-based strategies to community- and ecosystem-based strategies, resulting in an increase in the available information on mammalian communities and their management. This book provides a synthesis of the published literature on the role of forest mammals in community structure and function, with emphasis on their management and conservation. In addition to coverage of some of the charismatic megafauna such as grizzly bears, gray wolves, mountain lions, elk and moose, the book also provides a thorough treatment of small terrestrial mammals, arboreal rodents, bats, medium-sized carnivores, and ungulates. The unique blend of theoretical and practical concepts makes this book equally suitable for managers, educators, and research biologists who will find it a valuable reference to the recent literature on a vast array of topics on mammalian ecology.
Article
Full-text available
Factors related to timing of fawning in southern mule deer (Odocoileus hemionus fuliginatus) were studied in montane areas of San Diego Co., California, from June 1977 to January 1979. Mean date of birth was 27 June (SD = 7.4 days). Rates of nursing were highest in June and declined thereafter. Forage available to females following fawning was dry and of low quality; deer increased their consumption of browse during this period. A resultant shortened period of maternal care may have contributed to mortality (50-74%) of fawns. Timing of parturition may be related to the requirements of the maternal female during the last third of gestation when favorable temperatures and adequate precipitation combine to produce abundant forage. Sex ratios (males:females) changed markedly from yearlings (3.1:1) to adults (0.2:1), presumably because rutting activities followed harsh summer conditions.
Code
Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes. [Please do not request the full text - it is an R package. The up-to-date manual is available from CRAN].
Article
Presents a checklist of the 600 species of vascular plants.-from Authors
Article
As use of Akaike's Information Criterion (AIC) for model selection has become increasingly common, so has a mistake involving interpretation of models that are within 2 AIC units (ΔAIC ≤ 2) of the top-supported model. Such models are <2 ΔAIC units because the penalty for one additional parameter is 2 AIC units, but model deviance is not reduced by an amount sufficient to overcome the 2-unit penalty and, hence, the additional parameter provides no net reduction in AIC. Simply put, the uninformative parameter does not explain enough variation to justify its inclusion in the model and it should not be interpreted as having any ecological effect. Models with uninformative parameters are frequently presented as being competitive in the Journal of Wildlife Management, including 72 of all AIC-based papers in 2008, and authors and readers need to be more aware of this problem and take appropriate steps to eliminate misinterpretation. I reviewed 5 potential solutions to this problem: 1) report all models but ignore or dismiss those with uninformative parameters, 2) use model averaging to ameliorate the effect of uninformative parameters, 3) use 95 confidence intervals to identify uninformative parameters, 4) perform all-possible subsets regression and use weight-of-evidence approaches to discriminate useful from uninformative parameters, or 5) adopt a methodological approach that allows models containing uninformative parameters to be culled from reported model sets. The first approach is preferable for small sets of a priori models, whereas the last 2 approaches should be used for large model sets or exploratory modeling.
Article
Linear regression was used to relate snow accumulation during single and consecutive winters with white-tailed deer Odocoileus virginianus fawn:doe ratios, moose Alces alces twinning rates and calf:cow ratios, and annual changes in deer and moose populations. Significant relationships were found between snow accumulation during individual winters and these dependent variables during the following year, but strongest relationships were between the dependent variables and the sums of the snow accumulations over the previous three winters. The percentage of the variability explained was 36-51%. Significant relationships were also found between winter vulnerability of moose calves and the sum of the snow accumulations in the current, and up to 7 previous, winters, with 49% of the variability explained. These relationships imply that winter influences on maternal nutrition can accumulate for several years and that this cumulative effect strongly determines fecundity and/or calf and fawn survivability. Although wolf Canis lupus predation is the main direct mortality agent on fawns and calves, wolf density itself appears to be secondary to winter weather in influencing the deer and moose populations. -from Authors