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Paviolo et al 2009 Protection affect puma abundance and activity pattern.pdf

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Vol. 90, No. 4


TABLE 1.—Dates and sampling effort of the different camera-trap surveys of pumas (Puma concolor) conducted in the Green Corridor of
Misiones Province, Argentina.


No. stations

Iguazu´ 2004
Iguazu´ 2006–2007

April–December 2004
April 2006–January 2007
May 2003–February 2004
March–December 2005



Full survey duration (days) Full survey effort (trap-days) Total survey effort (trap-days)a



Pilot + full surveys.

camera-traps of different brands and models. The equipment
consisted of 2 Camtrakker (Camtrakker, Watkinsville, Georgia), 50 Leaf Rivers Trail Scan Model C-1 (Vibra Shine,
Taylorsville, Mississippi), 30 TrailMACs 35mm Standard
Game (Trail Sense Engineering, LLC, Middletown, Delaware), and 20 Trapacamera (CIETEC, Sa˜o Paulo, Brazil)
scouting cameras. Prior to the full survey period, we
conducted pilot surveys with the purpose of identifying the
best sites for the locations of the stations (Table 1). The full
surveys consisted of a period of 90–96 days (Table 1).
Because of the longevity and length of territory tenure of
pumas, we assumed that a survey of this duration fulfilled the
assumptions of a closed population (Karanth and Nichols
2002; Kelly et al. 2008).
We identified pumas following the protocol proposed by
Kelly et al. (2008). Three of the authors independently
classified the photographs of individuals, noting the distinguishing characteristics of each animal. After independent
classifications, the 3 authors compared results and discussed
their reasons for each classification, correcting discrepancies
in cases when 1 of the authors could find evidence that the
classification was incorrect. When the evidence was not clear
the authors maintained their independent classifications. After
this, we estimated the density of pumas using the classification
of the 3 authors.
We estimated puma abundance using the program CAPTURE (Rexstad and Burnham 1991), which provides
population estimates using several models (Otis et al. 1978;
White et al. 1982). We present the results of the model Mh
using jackknife estimates that assume heterogeneity in the
capture probability among individuals. This model is the most
appropriate because of the varying accessibility to the stations
among individuals, product of the social structure of the
population, and the location of the stations within each
individual’s home range (Karanth and Nichols 2002). We
divided the survey into capture occasions of 6 consecutive
days with the purpose of obtaining a capture probability .0.1
(Otis et al. 1978; White et al. 1982). Cubs (,1 year old) were
not included in this analysis because their capture probability
is related to the capture probability of their mothers (Karanth
and Nichols 2002). Consequently, our density estimates refer
to the population of adults and subadults.
To estimate density it is necessary to calculate the area
surveyed. Most authors suggest that the area surveyed must be
estimated by adding a buffer width equal to one-half the
average of the maximum distance between captures of the
individuals captured more than once during the survey (mean

maximum distance moved [MMDM]) to each camera or the
polygon that includes all the cameras (Karanth 1995; Silver et
al. 2004; Trolle and Kery 2003). However, Maffei and Noss
(2007) suggest that if the surveyed area covers ,4 mean home
ranges of the studied species, MMDM may be underestimated
and in turn the area surveyed may be underestimated. In these
situations, the appropriate buffer should be between one-half
MMDM and MMDM (Maffei and Noss 2007). Because we
lacked estimates of the size of puma home ranges for our study
areas, we estimated density using 2 different calculations of
the surveyed area: 1 was obtained by applying to each
sampling station a buffer of one-half MMDM, and the other by
applying a full MMDM buffer. We deducted those areas that
are not suitable habitats for pumas, such as cities, annual
crops, and airports. The value of MMDM was estimated as the
average of the maximum distance of recapture for individuals
captured at .1 station (Karanth 1995; Karanth and Nichols
2002), according to each investigator’s classification. The
values of MMDM and the surveyed areas were estimated
using the program ArcView (version 3.2; Environmental
Systems Research Institute, Inc., Redlands, California).
Some researchers have suggested that the photographic rate
of a species is correlated with its absolute abundance (Carbone
et al. 2001), especially when controlling for some confounding
factors (Di Bitetti et al. 2008a). In order to validate the
patterns observed using the density estimates, we compared
different indices of relative abundance among surveys and the
study areas. We used the recording rate of pumas (number of
photographs of pumas/1,000 trap-days), the mean number of
individuals recorded per station, and the percentage of stations
with puma presence as relative abundance indices. Because
the indices varied widely between roads and trails (see
‘‘Results’’), and because the number of stations located on
trails at Yabotı´ (only 1) was insufficient to make a bifactorial
analysis including this variable, we compared the abundance
indices using only the values obtained from the stations
located on roads. In the Iguazu´ 2006–2007 and Yabotı´ surveys
we compared the relative abundance indices of pumas
between the best-protected and the least-protected subareas.
In addition, we compared the indices between the Iguazu´ 2004
survey and the same area of the Iguazu´ 2006–2007 survey to
determine whether differences between years existed. Because
the relative abundance data were not normally distributed, we
used nonparametric statistics for these comparisons.
Activity pattern analysis.—To describe the activity pattern
of pumas, we used the time printed on the photographs
obtained during the pilot and full surveys (Table 1). We