ArticlePDF Available

Balancing sample accumulation and DNA degradation rates to optimize noninvasive genetic sampling of sympatric carnivores

Authors:
  • United States Geological Survey/Oklahoma State University
  • USDA/WS/National Wildlife Research Center
  • USDA/Wildlife Services/National Wildlife Research Center

Abstract and Figures

Noninvasive genetic sampling, or noninvasive DNA sampling (NDS), can be an effective monitoring approach for elusive, wide-ranging species at low densities. However, few studies have attempted to maximize sampling efficiency. We present a model for combining sample accumulation and DNA degradation to identify the most efficient (i.e., minimal cost per successful sample) NDS temporal design for capture-recapture analyses. We use scat accumulation and faecal DNA degradation rates for two sympatric carnivores, kit fox (Vulpes macrotis) and coyote (Canis latrans) across two seasons (summer and winter) in Utah, USA, to demonstrate implementation of this approach. We estimated scat accumulation rates by clearing and surveying transects for scats. We evaluated mitochondrial (mtDNA) and nuclear (nDNA) DNA amplification success for fecal DNA samples under natural field conditions for 20 fresh scats/species/season from <1-112 days. Mean accumulation rates were nearly three times greater for coyotes (0.076 scats/km/day) than foxes (0.029 scats/km/day) across seasons. Across species and seasons, mtDNA amplification success was ≥95% through day 21. Fox nDNA amplification success was ≥70% through day 21 across seasons. Coyote nDNA success was ≥70% through day 21 in winter, but declined to <50% by day 7 in summer. We identified a common temporal sampling frame of ~14 days that allowed species to be monitored simultaneously, further reducing time, survey effort and costs. Our results suggest that when conducting repeated surveys for capture-recapture analyses, overall cost-efficiency for NDS may be improved with a temporal design that balances field and laboratory costs along with deposition and degradation rates. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Content may be subject to copyright.
Balancing sample accumulation and DNA degradation rates
to optimize noninvasive genetic sampling of sympatric
carnivores
ROBERT C. LONSINGER,* ERIC M. GESE,†‡ STEVEN J. DEMPSEY,BRYAN M. KLUEVER,
TIMOTHY R. JOHNSON§and LISETTE P. WAITS*
*Department of Fish and Wildlife Sciences, University of Idaho, 875 Perimeter Drive MS1136, Moscow, ID 83844-1136, USA,
United States Department of Agriculture, Wildlife Services, National Wildlife Research Center, Logan, UT 84322-5230, USA,
Department of Wildland Resources, Utah State University, Logan, UT 84322-5230, USA, §Department of Statistical Science,
University of Idaho, 875 Perimeter Drive MS1104, Moscow, ID 83844-1104, USA
Abstract
Noninvasive genetic sampling, or noninvasive DNA sampling (NDS), can be an effective monitoring approach for
elusive, wide-ranging species at low densities. However, few studies have attempted to maximize sampling effi-
ciency. We present a model for combining sample accumulation and DNA degradation to identify the most efficient
(i.e. minimal cost per successful sample) NDS temporal design for capturerecapture analyses. We use scat accumula-
tion and faecal DNA degradation rates for two sympatric carnivores, kit fox (Vulpes macrotis) and coyote (Canis
latrans) across two seasons (summer and winter) in Utah, USA, to demonstrate implementation of this approach. We
estimated scat accumulation rates by clearing and surveying transects for scats. We evaluated mitochondrial
(mtDNA) and nuclear (nDNA) DNA amplification success for faecal DNA samples under natural field conditions for
20 fresh scats/species/season from <1112 days. Mean accumulation rates were nearly three times greater for coyotes
(0.076 scats/km/day) than foxes (0.029 scats/km/day) across seasons. Across species and seasons, mtDNA amplifica-
tion success was 95% through day 21. Fox nDNA amplification success was 70% through day 21 across seasons.
Coyote nDNA success was 70% through day 21 in winter, but declined to <50% by day 7 in summer. We identified a
common temporal sampling frame of approximately 14 days that allowed species to be monitored simultaneously,
further reducing time, survey effort and costs. Our results suggest that when conducting repeated surveys for cap-
turerecapture analyses, overall cost-efficiency for NDS may be improved with a temporal design that balances field
and laboratory costs along with deposition and degradation rates.
Keywords:Canis latrans, DNA degradation, genotyping error, noninvasive genetic sampling, scat deposition, Vulpes
macrotis
Received 15 September 2014; revision received 26 November 2014; accepted 28 November 2014
Introduction
Noninvasive genetic sampling, or noninvasive DNA
sampling (NDS), is increasingly being used to monitor
species that are rare, elusive or otherwise difficult to sur-
vey with traditional techniques (Waits & Paetkau 2005).
Genetic material obtained from noninvasive sources (e.g.
faeces, hair, feathers) can allow for species identification
and individual identification, population genetic
structure, genetic diversity, connectivity and sex ratios
(Beja-Pereira et al. 2009). Combining NDS with capture
recapture and occupancy modelling approaches allows
researchers to estimate population demographic parame-
ters (Lukacs & Burnham 2005) and patterns of occur-
rence (Long et al. 2011). Many studies have opted for
NDS due to logistical and animal welfare considerations,
or improved cost-benefits (e.g. Prugh et al. 2005; Brøseth
et al. 2010; Stenglein et al. 2010b).
DNA degradation and genotyping errors can influ-
ence NDS results (Taberlet et al. 1999; Waits & Paetkau
2005; Beja-Pereira et al. 2009). Accordingly, researchers
have expended considerable effort to understand how
factors such as sample age (Piggott 2004; Murphy et al.
2007; Santini et al. 2007), environmental conditions (Pig-
gott 2004; Murphy et al. 2007; Santini et al. 2007; DeMay
et al. 2013), diet (Murphy et al. 2003; Panasci et al. 2011),
sample collection and storage techniques (Murphy et al.
Correspondence: Robert C. Lonsinger, Fax: 208-885-9080;
E-mail: Lons1663@vandals.uidaho.edu
©2014 John Wiley & Sons Ltd
Molecular Ecology Resources (2014) doi: 10.1111/1755-0998.12356
2002; Palomares et al. 2002; Piggott & Taylor 2003; Steng-
lein et al. 2010a; Panasci et al. 2011), locus length (Buchan
et al. 2005; DeMay et al. 2013) and species-specific differ-
ences (Piggott & Taylor 2003; Buchan et al. 2005) influ-
ence the degradation of DNA. Collectively these studies
indicate DNA degradation and genotyping errors vary
among species and environmental conditions. General
recommendations to reduce degradation and genotyping
errors included sampling the freshest scats and conduct-
ing surveys during the driest and/or coldest seasons
(Murphy et al. 2007; Santini et al. 2007).
While previous efforts to optimize NDS have focused
on ways to minimize DNA degradation and genotyping
errors, they have not explicitly incorporated sample
accumulation rates. Understanding sample accumulation
rates (i.e. the rate at which noninvasive genetic samples
accrue and can be obtained) is critical to designing effi-
cient sampling and may influence the optimal temporal
sampling frame. Faecal DNA is a common source of non-
invasive genetic samples, but sample accumulation rate
is probably affected by diet, behaviour, physiology and
environmental conditions. For example, seasonal varia-
tion in diet, behaviour and space use by carnivores can
influence scat deposition rates and patterns (Andelt &
Andelt 1984; Ralls et al. 2010). Additionally, heavy rain
or winds can remove scats, as can conspecifics (Living-
ston et al. 2005).
The temporal sampling design of NDS can be opti-
mized to maximize laboratory success while minimizing
overall cost per successful sample. Laboratory costs are
driven by the number of samples collected, polymerase
chain reaction (PCR) success rates and genotyping error
rates (Fig. 1). Scat accumulation rates, survey effort (spa-
tial coverage), desired sample size (number of samples
required to achieve objectives) and the number of sam-
pling events (temporal frequency) necessary to achieve
the desired sample size influence field costs (Fig. 1).
Thus, to optimize the temporal design for NDS, pilot
studies should consider both laboratory and field costs
by incorporating DNA degradation and sample accumu-
lation rates for each species, season and study site.
Here, we present a model for combining information
on sample accumulation and DNA degradation to opti-
mize (i.e. identify the most cost-effective) temporal sam-
pling design for capturerecapture studies employing
NDS. We use scat accumulation rates and faecal DNA
degradation rates for two sympatric carnivores, kit foxes
(Vulpes macrotis; hereafter foxes) and coyotes (Canis la-
trans), across two seasons in the Great Basin desert of
Utah, USA, to demonstrate how this approach can be
implemented. In regards to scat accumulation, we
hypothesized that (i) scat accumulation would be greater
for coyotes than foxes due to their more omnivorous diet
and higher abundance and (ii) seasonal variation in diets
would result in higher accumulation rates in summer
than winter for both species (Andelt & Andelt 1984; Arjo
et al. 2007; Kozlowski et al. 2008). Regarding DNA degra-
dation, we hypothesized that (i) due to its higher relative
abundance mitochondrial DNA (mtDNA) would have
higher PCR (or amplification) success rates than nuclear
DNA (nDNA), (ii) amplification success would decrease
over time for both nDNA and mtDNA, (iii) amplification
success would decrease more precipitously for nDNA
than mtDNA and (iv) amplification success for nDNA
would be higher for shorter microsatellite loci than
longer loci (Buchan et al. 2005; DeMay et al. 2013).
Materials and methods
Study area
Our investigation took place on the U.S. Army Dugway
Proving Ground (DPG), in western Utah. Located within
the Great Basin, DPG is characterized by basin and range
formations with elevations from 1228 to 2154 m (Arjo
et al. 2007). The site experiences cold winters and moder-
ate summers; coldest and warmest months are January
(mean high =3.3 °C, mean low =8.8 °C) and July
(mean high =34.7 °C, mean low =16.3 °C), respectively.
Mean annual precipitation is approximately 20 cm with
the greatest rainfall occurring in spring (Arjo et al. 2007).
Sampling seasons corresponded to periods preceding
breeding (January and February) and juvenile dispersal
(July and August) for target species and aligned with
periods of reduced precipitation in the region (Arjo et al.
2007).
Fig. 1 Conceptual diagram showing the major components
required to balance field and laboratory efficiency for optimiza-
tion of noninvasive genetic sampling for capturerecapture
analysis.
©2014 John Wiley & Sons Ltd
2R. C. LONSINGER ET AL.
Sample accumulation surveys
Scat accumulation surveys in which transects are cleared
and surveyed approximately 14 days later are commonly
used to estimate relative abundances of canids (Gese
2001; Schauster et al. 2002). Using this approach, we con-
ducted scat accumulation surveys between September
2010 and July 2012. Scat surveys were originally initiated
to evaluate relative abundance of foxes and coyotes and
therefore data were available not only for our winter and
summer sampling seasons, but also for spring. Fifteen
5 km transects along dirt or gravel roads were cleared
and surveyed for carnivore scats approximately 14 days
later (mean =13.9 0.51 SD, range =1316). Each 5 km
transect was surveyed during two summers (2010, 2011),
two springs (2011, 2012) and one winter (2011). Addition-
ally, to expand the spatial coverage and ensure that stan-
dardized accumulation rates (scats/km/day) were
similar between sampling intervals of different durations,
we evaluated scat accumulation along eight shorter tran-
sects during one summer (2012), using a random starting
point, direction and length (mean =2.6 0.85 SD,
range =13.5 km) and surveying 7 days after clearing.
We determined species for each carnivore scat detected
during accumulation surveys based on overall appear-
ance, size and shape (Kozlowski et al. 2012).
Faecal DNA degradation
Faecal DNA degradation was assessed at DPG during
two seasons, winter (initiated 8 February 2012) and sum-
mer (initiated 11 July 2012), corresponding to proposed
field sampling seasons. In each season, 20 fresh scats
were collected per species. Fox scats were obtained from
live-captured, free-ranging individuals, and coyote scats
were obtained from the USDA/NWRC/Predator
Research Facility (Millville, UT, USA). Scats were frozen
within four hours of collection. On average, fox and coy-
ote scats were stored frozen for 18 months and
<1 month, respectively, before being transferred to the
study site, thawed and placed in the field and protected
from disturbance with a frame covered with wire mesh
(25 mm openings; 0.7 gauge wire). We collected faecal
DNA samples from each scat at days 1, 3, 7, 14, 21, 56
and 112, or until the scat was fully utilized. Day 1 sam-
ples were collected just prior to exposure to field condi-
tions. We added a day 5 time point during summer to
provide greater resolution, as a recent study detected a
significant decline in coyote faecal DNA quality as early
as 5 days postdeposition (Panasci et al. 2011). Addition-
ally, a severe wind event during winter buried experi-
mental plots after day 21, so day 56 and 112 time points
were only available for summer. Faecal DNA samples
were collected from the side of each scat following
procedures of Stenglein et al. (2010a), and scats were
considered fully utilized when no additional samples
could be collected in this manner. All samples were
stored in 1.4 mL of DET buffer (20% DMSO, 0.25 M
EDTA, 100 lMTris, pH 7.5 and NaCl to saturation; Seu-
tin et al. 1991). Due to natural variability in scat sizes,
some smaller scats were fully utilized before completion
of all time points, resulting in reduced sample sizes at
later time points. To maintain more equitable sample
sizes among time points during summer, we placed
three additional scats for each species out at the start of
the degradation study and sampled these scats in place
of fully utilized scats at later time points.
DNA extraction and PCR amplification
We conducted faecal DNA extraction and PCR amplifica-
tion in a facility dedicated to low-quality DNA. Faecal
DNA samples were extracted using the QIAamp DNA
Stool Mini Kits (Qiagen, Inc., Valencia, CA, USA) with
negative controls to monitor for contamination (Taberlet
& Luikart 1999; Beja-Pereira et al. 2009). We performed
mtDNA species identification tests by amplifying frag-
ments of the control region (Onorato et al. 2006; De Barba
et al. 2014). Species-specific PCR products lengths were
336337 base pairs (bp) for foxes and 115120 bp and
360364 bp for coyotes (De Barba et al. 2014). Samples
that failed to amplify for mtDNA were repeated once to
minimize sporadic effects (Murphy et al. 2007). For indi-
vidual identification, we amplified fox and coyote sam-
ples with seven and nine nDNA microsatellite loci,
respectively (Appendix S1, Supporting information). We
conducted PCR on a Bio-Rad Tetrad thermocycler (Bio-
Rad, Hercules, CA, USA) including negative and posi-
tive controls. PCR conditions, including primer concen-
trations and thermal profiles, are presented in Appendix
S1 (Supporting information). We visualized results using
a 3130xl DNA Analyzer (Applied Biosystems, Foster
City, CA, USA) and scored allele sizes with Genemapper
3.7 (Applied Biosystems). Samples were considered suc-
cessful for species identification if amplification of
1 mtDNA fragment was achieved in either the first or
second amplification attempt. We calculated mtDNA
success rates as the proportion of successful samples
across each time point and season. We calculated nDNA
amplification success rates (number of successful ampli-
fications/total possible) and sample success rates (pro-
portion of samples that amplified at 50% of the loci) for
each time point and species.
Genotyping error rates
We combined replicates for each scat (i.e. all replicates
across time points with successful nDNA amplification)
©2014 John Wiley & Sons Ltd
OPTIMIZING NONINVASIVE GENETIC SAMPLING 3
to establish consensus genotypes (Taberlet et al. 1999;
Pompanon et al. 2005). To achieve a consensus genotype,
we required that heterozygote and homozygote alleles
be observed in two and three independent replicates,
respectively. Following the methods of Broquet & Petit
(2004), we classified the observation of an allele not pres-
ent in the consensus genotype as a false allele (FA) and
the amplification of only one allele in a heterozygous
consensus genotype as allelic dropout (ADO).
Data analysis
Scat accumulation results were standardized across tran-
sects and species as daily accumulation rates (scats accu-
mulated/days since clearing =scats/km/day). We
employed a generalized linear model to test effects of
season and species on scat accumulation (O’Hara & Ko-
tze 2010). We considered a Poisson regression model
with a log link function, but residuals indicated under-
dispersion so we based inferences on quasi-likelihood
with a free dispersion parameter. We used a likelihood
ratio test to compare models with and without interac-
tions. We compared the influence of main effects and fac-
tor levels with contrast analysis (Rpackage contrast;
Kuhn et al. 2011; R Core Team 2014).
We evaluated PCR success, FA and ADO as binary
response variables with mixed-effects logistic regression
models to assess DNA degradation rates, with sample
included as a random effect to resolve pseudoreplication
effects due to multiple observations per sample with SAS
9.3 (SAS Institute Inc. 2011). We included time since the
scat was placed in the field (log transformed), DNA type
(mtDNA vs. nDNA), species (fox vs. coyote), season
(winter vs. summer) and locus length as fixed effects in
the model for PCR success. We excluded DNA type from
models for FA and ADO as these pertain only to nDNA.
We categorized nDNA locus lengths based on the mid-
length of alleles per locus by species (range: 90275 bp).
Optimization of NDS temporal design
Our goal was to optimize a NDS temporal design that
could be employed within a capturerecapture frame-
work for foxes and coyotes. To this end, we derived a
total cost per successful sample (i.e. sample that achieves
a consensus genotype for individual identification) at
sampling intervals from 1 to 56 days, where the interval
represented the number of days between clearing and
survey or between sequential surveys.
Both spatial survey effort and desired sample size
must be selected by the researcher, but may be informed
by previous research, power analyses and/or simula-
tions (Williams et al. 2002). We selected a survey effort of
150 km, a length of transect which we felt provided
reasonable coverage of our study site and encompasses
1350 km
2
within 2.5 km of transects, the radius of the
average fox home range at DPG (Dempsey 2013). We
identified desired sample sizes of 200 fox and 400 coyote
samples, values approximately three times the number
of individuals expected to be in our study area (Solberg
et al. 2006).
We determined the number of samples accumulated
and available for collection at each potential sampling
interval (156 days, hereafter interval), by calculating the
product of the daily accumulation rate (scats/km/day),
the number of kilometres surveyed (effort) and the num-
ber of days in the interval. We combined the number of
samples accumulated at each interval with our model-
predicted PCR success rates to calculate the number of
successful samples for each interval, considering that
each interval contained scats of varying ages and levels
of degradation. For example, for an interval of 3 days,
we assumed that 33.3% of the scats were 1, 2 and 3 days
old and that each age class was characterized by its
model-predicted PCR success.
Noninvasive samples commonly suffer from genotyp-
ing errors (Pompanon et al. 2005), which can influence
costs. For each interval, we summed the model-predicted
FA and ADO rates to determine the overall predicted
genotyping error rate. We then calculated the number of
genotyping errors expected for samples on each day as
the entrywise product of the number of successful sam-
ples and the predicted genotyping error rate for that day.
The total number of samples, with a genotyping error
within a given interval then, was the sum of the number
of samples with a genotyping error across all days con-
tributing to the interval. The cumulative genotyping
error rate for an interval was determined as the propor-
tion of successful samples with a genotyping error.
As genotyping errors increase, additional replicates
are required to reconcile differences among genotypes
(Pompanon et al. 2005). Within a capturerecapture
framework, errors in multilocus genotypes can result in
overestimates of abundance and bias survival estimates
(Lukacs & Burnham 2005). Consequently, we set a goal
of maintaining a probability of error 2% in our data set.
We assumed genotyping error rate was similar across
loci, and replicates were independent. We calculated the
probability of having an error in the consensus genotype
at a given interval as the cumulative genotyping error
rate raised to the number of replicates, then multiplied
by the number of loci. We estimated our laboratory costs
to be approximately $60/sample (including labour and
supplies for extraction, four independent amplifications
and finalization of the consensus genotype), based on
current laboratory expenses, with a 25% increase in cost
for each additional pair of replicates. Thus, when the
number of replicates required to maintain our goal of
©2014 John Wiley & Sons Ltd
4R. C. LONSINGER ET AL.
to establish consensus genotypes (Taberlet et al. 1999;
Pompanon et al. 2005). To achieve a consensus genotype,
we required that heterozygote and homozygote alleles
be observed in two and three independent replicates,
respectively. Following the methods of Broquet & Petit
(2004), we classified the observation of an allele not pres-
ent in the consensus genotype as a false allele (FA) and
the amplification of only one allele in a heterozygous
consensus genotype as allelic dropout (ADO).
Data analysis
Scat accumulation results were standardized across tran-
sects and species as daily accumulation rates (scats accu-
mulated/days since clearing =scats/km/day). We
employed a generalized linear model to test effects of
season and species on scat accumulation (O’Hara & Ko-
tze 2010). We considered a Poisson regression model
with a log link function, but residuals indicated under-
dispersion so we based inferences on quasi-likelihood
with a free dispersion parameter. We used a likelihood
ratio test to compare models with and without interac-
tions. We compared the influence of main effects and fac-
tor levels with contrast analysis (Rpackage contrast;
Kuhn et al. 2011; R Core Team 2014).
We evaluated PCR success, FA and ADO as binary
response variables with mixed-effects logistic regression
models to assess DNA degradation rates, with sample
included as a random effect to resolve pseudoreplication
effects due to multiple observations per sample with SAS
9.3 (SAS Institute Inc. 2011). We included time since the
scat was placed in the field (log transformed), DNA type
(mtDNA vs. nDNA), species (fox vs. coyote), season
(winter vs. summer) and locus length as fixed effects in
the model for PCR success. We excluded DNA type from
models for FA and ADO as these pertain only to nDNA.
We categorized nDNA locus lengths based on the mid-
length of alleles per locus by species (range: 90275 bp).
Optimization of NDS temporal design
Our goal was to optimize a NDS temporal design that
could be employed within a capturerecapture frame-
work for foxes and coyotes. To this end, we derived a
total cost per successful sample (i.e. sample that achieves
a consensus genotype for individual identification) at
sampling intervals from 1 to 56 days, where the interval
represented the number of days between clearing and
survey or between sequential surveys.
Both spatial survey effort and desired sample size
must be selected by the researcher, but may be informed
by previous research, power analyses and/or simula-
tions (Williams et al. 2002). We selected a survey effort of
150 km, a length of transect which we felt provided
reasonable coverage of our study site and encompasses
1350 km
2
within 2.5 km of transects, the radius of the
average fox home range at DPG (Dempsey 2013). We
identified desired sample sizes of 200 fox and 400 coyote
samples, values approximately three times the number
of individuals expected to be in our study area (Solberg
et al. 2006).
We determined the number of samples accumulated
and available for collection at each potential sampling
interval (156 days, hereafter interval), by calculating the
product of the daily accumulation rate (scats/km/day),
the number of kilometres surveyed (effort) and the num-
ber of days in the interval. We combined the number of
samples accumulated at each interval with our model-
predicted PCR success rates to calculate the number of
successful samples for each interval, considering that
each interval contained scats of varying ages and levels
of degradation. For example, for an interval of 3 days,
we assumed that 33.3% of the scats were 1, 2 and 3 days
old and that each age class was characterized by its
model-predicted PCR success.
Noninvasive samples commonly suffer from genotyp-
ing errors (Pompanon et al. 2005), which can influence
costs. For each interval, we summed the model-predicted
FA and ADO rates to determine the overall predicted
genotyping error rate. We then calculated the number of
genotyping errors expected for samples on each day as
the entrywise product of the number of successful sam-
ples and the predicted genotyping error rate for that day.
The total number of samples, with a genotyping error
within a given interval then, was the sum of the number
of samples with a genotyping error across all days con-
tributing to the interval. The cumulative genotyping
error rate for an interval was determined as the propor-
tion of successful samples with a genotyping error.
As genotyping errors increase, additional replicates
are required to reconcile differences among genotypes
(Pompanon et al. 2005). Within a capturerecapture
framework, errors in multilocus genotypes can result in
overestimates of abundance and bias survival estimates
(Lukacs & Burnham 2005). Consequently, we set a goal
of maintaining a probability of error 2% in our data set.
We assumed genotyping error rate was similar across
loci, and replicates were independent. We calculated the
probability of having an error in the consensus genotype
at a given interval as the cumulative genotyping error
rate raised to the number of replicates, then multiplied
by the number of loci. We estimated our laboratory costs
to be approximately $60/sample (including labour and
supplies for extraction, four independent amplifications
and finalization of the consensus genotype), based on
current laboratory expenses, with a 25% increase in cost
for each additional pair of replicates. Thus, when the
number of replicates required to maintain our goal of
©2014 John Wiley & Sons Ltd
4R. C. LONSINGER ET AL.
degradation (Table 2). We detected significant interac-
tions between the fixed effects of time and both season
and locus length. PCR success for mtDNA and nDNA
declined more slowly in winter than summer, and
nDNA success declined more precipitously for longer
loci than shorter loci (Fig. 3). Significant interactions
Table 1 Generalized linear model and contrast analysis results with standard errors (SE) and lower (LL) and upper (UL) 95% confi-
dence bounds for scat accumulation samples collected from September 2012 to July 2012 at Dugway Proving Ground, Utah. Species lev-
els include coyote (Canis latrans) and kit fox (Vulpes macrotis). Season levels include spring, summer and winter
Estimate SE z-value P-value LL UL
Model parameters
(Intercept) 3.01 0.243 12.37 <0.001*3.52 2.56
Summer 0.66 0.277 2.38 0.019*0.13 1.22
Winter 0.47 0.349 1.36 0.177 0.23 1.16
Kit fox 0.97 0.253 3.83 <0.001*1.49 0.49
Contrasts
Coyote vs. Kit fox 1.08 0.119 9.09 <0.001*1.32 0.85
Summer vs. Winter 0.26 0.137 1.89 0.059 0.01 0.53
Summer vs. Spring 0.79 0.131 5.99 <0.001*0.53 1.04
Spring vs. Winter 0.53 0.167 3.16 0.002*0.85 0.19
Significant (*)P-values for zstatistic evaluated at a=0.05.
Table 2 Mixed-effects logistic regression model results for PCR success, allelic dropout and false alleles for kit fox (Vulpes macrotis) and
coyote (Canis latrans) faecal DNA samples collected in 2012 during winter and summer at Dugway Proving Ground, Utah. Reported
chi-square test statistics and P-values were generated with Type III tests of fixed effects
Fixed effect
PCR success Allelic dropout False alleles
Chi-square P-value Chi-square P-value Chi-square P-value
Time 4.93 0.0263*0.80 0.3706 0.09 0.7678
DNA type 224.06 <0.0001*————
Locus length 8.73 0.0031*0.03 0.8661 1.26 0.2614
Season 4.02 0.0449*4.11 0.0427*0.93 0.3337
Species 25.90 <0.0001*0.64 0.4237 7.95 0.0048*
Time 9Season 42.02 <0.0001*0.28 0.5966 5.91 0.0150*
Time 9Species 24.15 <0.0001*4.09 0.0432*4.94 0.0262*
Time 9Locus length 13.38 0.0003*1.03 0.3100 0.04 0.8386
Locus length 9Season 1.57 0.2100 1.22 0.2699 0.15 0.7020
Locus length 9Species 8.36 0.0038*1.57 0.2098 10.16 0.0014
Significance (*) was evaluated at a=0.05. Time was log-transformed days since the scat was placed in the field. DNA types included
mitochondrial and nuclear DNA. Locus length was based on the midpoint of each locus (range 90275 base pairs).
Kit fox Co
y
ote
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Summer Winter
0306090 0306090
Time (Days)
Probability of PCR success
nDNA (80 bp)
nDNA (130 bp)
nDNA (180 bp)
nDNA (230 bp)
nDNA (280 bp)
mtDNA
Fig. 3 Mixed-effects logistic regression
model results for PCR success for kit fox
(Vulpes macrotis) and coyote (Canis latrans)
faecal DNA samples collected in 2012
during winter and summer at Dugway
Proving Ground, Utah.
©2014 John Wiley & Sons Ltd
6R. C. LONSINGER ET AL.
were detected between species and both time and locus
length (Table 2).
Genotyping error rates
Overall genotyping error rates varied between species
(Fig. S2, Supporting information); across seasons and
sampling periods, overall ADO was lower for foxes
(18%) than coyotes (25%), while overall FA rate was
slightly higher for foxes (5%) than coyotes (2%). Win-
ter samples of both species had lower genotyping
error rates on average than summer samples. Fox win-
ter ADO rates ranged from 4% to 36%, whereas fox
summer ADO rates ranged from 15% to 42% (Fig. S2,
Supporting information). Coyote ADO rates ranged
from 10% to 29% in winter and 15% to 56% in sum-
mer (Fig. S2, Supporting information). In both seasons,
FA rates were low for both species (Fig. S2, Support-
ing information). Models for ADO and FA suggested
that season and species, respectively, were the only
main effects influencing each model (Table 2). Model
results for ADO were influenced by a significant inter-
action between time and species, while model results
for FA were influenced by significant interactions of
time with season and species, and locus length with
species (Table 2). Model-predicted cumulative genotyp-
ing error rates (combined ADO and FA rates across
loci and intervals) were lower for foxes (winter
mean =20.9 0.6% SE; summer mean =25.1 0.6%
SE) than coyotes (winter mean =31.5 0.6% SE; sum-
mer mean =37.4 0.5% SE) and higher in summer
than winter for both species.
Optimization of NDS temporal design
For fox, the predicted number of samples accumulated
ranged from 4.1 (interval =1 day) to 226.8 (inter-
val =56 days) in winter and 6.2 (interval =1 day) to
345.0 (interval =56 days) in summer. The predicted
number of coyote samples accumulated ranged from
12.5 (interval =1 day) to 697.2 (interval =56 days) in
winter and 13.5 (interval =1 day) to 756.0 (inter-
val =56 days) in summer. For both species, the number
of samples predicted to fail for nDNA microsatellite
amplification, however, increased as interval length
increased (Fig. S3, Supporting information). Across sea-
sons and time points, a greater proportion of accumu-
lated coyote samples were predicted to fail than fox
samples (Fig. S3, Supporting information).
Based on model-predicted genotyping error rates, our
goal of 2% probability of error in the data set could be
achieved for fox with five or fewer replicates at all inter-
vals, with four replicates being sufficient up to 34 days
in winter and 16 days in summer. To achieve this goal
for coyotes, up to seven replicates were required. In win-
ter, five replicates were required for intervals of 3
16 days, six replicates for intervals of 1749 days and
seven replicates for intervals 50 days. For summer coy-
ote samples, the minimum number of replicates required
was five (13 days). Six replicates were required for
intervals of 417 days and seven replicates for intervals
of 18 days.
The number of sampling events necessary to obtain
desired sample sizes was initially high due to the low
number of samples accumulating over shorter intervals,
but declined precipitously (Fig. 4). The number of sam-
pling events was higher initially in winter than summer
for both species due to seasonal differences in accumula-
tion. The number of sampling events required was typi-
cally greater for foxes than coyotes despite the smaller
desired sample size; this difference was greater in sum-
mer than winter (Fig. 4).
Overall cost per successful sample showed a similar
pattern across species and seasons, but with differ-
ences in the magnitude and timing of changes. Cost
per successful sample was highest for both species
and seasons at the shortest intervals and was higher
for foxes (Fig. 4a) than coyotes (Fig. 4b) at shorter
intervals. For both species, cost per successful sample
was higher in winter than summer at short intervals.
Summer cost per successful sample surpassed winter
costs at 7 days for coyotes and 16 days for foxes.
Costs per successful sample declined as the number of
required sampling events reduced field costs, until
genotyping errors were sufficiently high to require
additional replicates, increasing laboratory costs. The
overall lower cumulative genotyping error resulted in
smaller increases in overall cost for foxes (Fig. 4a) rela-
tive to coyotes (Fig. 4b). Sharp increases in cost associ-
ated with additional replicates occurred at a shorter
interval for foxes (35 days) than coyotes (50 days) in
winter. In summer, sharp increases in cost associated
with additional replicates occurred at the same inter-
val (17 days) for both species. When surveying species
simultaneously, overall cost per successful sample was
reduced (Fig. 4c) for each species, due to reduced field
costs for each species individually. Average annual
cost per successful sample suggested that a temporal
sampling frame of approximately 14 days would
reduce costs for each species and allow species to be
monitored simultaneously (Fig. 4c).
Discussion
Our study is among the first to incorporate DNA
degradation and sample accumulation rates to opti-
mize NDS design; a similar approach was recently
applied to ungulates (Woodruff et al. in press). Our
©2014 John Wiley & Sons Ltd
OPTIMIZING NONINVASIVE GENETIC SAMPLING 7
degradation (Table 2). We detected significant interac-
tions between the fixed effects of time and both season
and locus length. PCR success for mtDNA and nDNA
declined more slowly in winter than summer, and
nDNA success declined more precipitously for longer
loci than shorter loci (Fig. 3). Significant interactions
Table 1 Generalized linear model and contrast analysis results with standard errors (SE) and lower (LL) and upper (UL) 95% confi-
dence bounds for scat accumulation samples collected from September 2012 to July 2012 at Dugway Proving Ground, Utah. Species lev-
els include coyote (Canis latrans) and kit fox (Vulpes macrotis). Season levels include spring, summer and winter
Estimate SE z-value P-value LL UL
Model parameters
(Intercept) 3.01 0.243 12.37 <0.001*3.52 2.56
Summer 0.66 0.277 2.38 0.019*0.13 1.22
Winter 0.47 0.349 1.36 0.177 0.23 1.16
Kit fox 0.97 0.253 3.83 <0.001*1.49 0.49
Contrasts
Coyote vs. Kit fox 1.08 0.119 9.09 <0.001*1.32 0.85
Summer vs. Winter 0.26 0.137 1.89 0.059 0.01 0.53
Summer vs. Spring 0.79 0.131 5.99 <0.001*0.53 1.04
Spring vs. Winter 0.53 0.167 3.16 0.002*0.85 0.19
Significant (*)P-values for zstatistic evaluated at a=0.05.
Table 2 Mixed-effects logistic regression model results for PCR success, allelic dropout and false alleles for kit fox (Vulpes macrotis) and
coyote (Canis latrans) faecal DNA samples collected in 2012 during winter and summer at Dugway Proving Ground, Utah. Reported
chi-square test statistics and P-values were generated with Type III tests of fixed effects
Fixed effect
PCR success Allelic dropout False alleles
Chi-square P-value Chi-square P-value Chi-square P-value
Time 4.93 0.0263*0.80 0.3706 0.09 0.7678
DNA type 224.06 <0.0001*————
Locus length 8.73 0.0031*0.03 0.8661 1.26 0.2614
Season 4.02 0.0449*4.11 0.0427*0.93 0.3337
Species 25.90 <0.0001*0.64 0.4237 7.95 0.0048*
Time 9Season 42.02 <0.0001*0.28 0.5966 5.91 0.0150*
Time 9Species 24.15 <0.0001*4.09 0.0432*4.94 0.0262*
Time 9Locus length 13.38 0.0003*1.03 0.3100 0.04 0.8386
Locus length 9Season 1.57 0.2100 1.22 0.2699 0.15 0.7020
Locus length 9Species 8.36 0.0038*1.57 0.2098 10.16 0.0014
Significance (*) was evaluated at a=0.05. Time was log-transformed days since the scat was placed in the field. DNA types included
mitochondrial and nuclear DNA. Locus length was based on the midpoint of each locus (range 90275 base pairs).
Kit fox Co
y
ote
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Summer Winter
0306090 0306090
Time (Days)
Probability of PCR success
nDNA (80 bp)
nDNA (130 bp)
nDNA (180 bp)
nDNA (230 bp)
nDNA (280 bp)
mtDNA
Fig. 3 Mixed-effects logistic regression
model results for PCR success for kit fox
(Vulpes macrotis) and coyote (Canis latrans)
faecal DNA samples collected in 2012
during winter and summer at Dugway
Proving Ground, Utah.
©2014 John Wiley & Sons Ltd
6R. C. LONSINGER ET AL.
with winter samples showing less DNA degradation
than summer samples. Piggott (2004) documented higher
faecal DNA degradation rates in winter than summer
and attributed this to increased moisture during winter.
Previous studies indicate that environmental conditions
such as temperature, UV exposure and humidity influ-
ence DNA degradation rates (Nsubuga et al. 2004; Mur-
phy et al. 2007; Stenglein et al. 2010a). Winters and
summers at DPG receive less precipitation than other
seasons, but temperatures are significantly different (see
Study area) and UV exposure is highest in summer. Our
study design did not allow investigation of the influence
of weather on degradation. We placed all samples in the
field on the same day each season, and therefore,
weather and time were confounded. We suspect though,
that differences observed between seasons were related
to broad differences in environmental conditions.
Our observed ADO and FA rates were similar to those
reported in other canid studies (Piggott 2004; Santini et al.
2007; Stenglein et al. 2010b; Panasci et al. 2011). We were
unable to detect a significant effect of time on genotyping
errors, but this was likely due to small sample sizes associ-
ated with ADO and FA models. We observed a discern-
ible, but not statistically significant increase in model-
predicted ADO rates over time, but not in FA rates.
Optimization of NDS temporal design
By balancing sample accumulation and DNA degrada-
tion, an optimal NDS design can be selected that mini-
mizes cost per successful sample. The optimal interval
varies by species and season and is driven by sample
collection (field) and processing (laboratory) costs.
While the optimal interval is simply the interval that
minimizes the cost per successful sample, additional
factors should be considered such as the number of
target species and interspecific differences in sample
accumulation and DNA degradation. Initial costs per
successful sample were calculated for sampling species
independently (Fig. 4a,b). If a common interval is
selected for foxes and coyotes, both species can be sur-
veyed simultaneously on the same transects and over-
all field costs can be reduced (Fig. 4c). Additionally,
selection of the optimal interval should consider
downstream analyses. For example, demographic clo-
sure assumptions may be difficult to meet at extended
intervals and small reductions in the cost per success-
ful sample may be insufficient justification to select
extended intervals.
Our results indicate a range of intervals for foxes
and coyotes could be selected to improve efficiency,
and these intervals are shorter in summer than winter.
For example, summer cost per successful sample was
minimized for foxes at day 14 and coyotes at day 9,
but selection of an interval 2 days from these opti-
mal intervals changed the cost per successful sample
by <$1. The range of effective intervals was wider in
winter. Winter cost per successful sample was mini-
mized for foxes and coyotes at days 34 and 24, respec-
tively, yet the cost per successful sample changed <$1
for intervals up to 8 days shorter (2533 days) for
foxes and for 24 intervals surrounding (1640 days)
the optimal interval for coyotes. We were interested in
selecting a common interval that was effective for both
species and consistent across seasons. Summer cost
per successful sample limited the upper bound of the
common interval, as cost increased sharply for both
species after day 17. We thus identified an interval of
14 days as the common interval within our system
(Fig. 4c). At 14 days, winter cost per successful sample
was reduced and continuing to decline slowly for both
species and the number of sampling events was small
enough to conduct sampling over a single season.
Based on these results, we recommend NDS efforts
account for sample accumulation and DNA degradation
during the design phase (Fig. 1). Previous studies have
recommended sampling the freshest scats possible (Mur-
phy et al. 2007; Santini et al. 2007; DeMay et al. 2013).
Our results show that when sampling over time within a
capturerecapture framework, short intervals may be
cost-prohibitive if a substantial sample size is required.
Thus, we recommend sampling designs consider cost
per successful sample and minimize violations of
assumptions for downstream analyses.
Limitations and implications for research
Collection of fresh samples (e.g. samples known to be
1 day old) to evaluate DNA degradation is logistically
prohibitive, particularly when species are rare, elusive,
or difficult to capture. Consequently, many studies
comparing PCR success (e.g. between species, under
environmental variations, over time) have relied on
samples from captive populations (Murphy et al. 2002,
2003, 2007; Piggott 2004; Santini et al. 2007; DeMay et al.
2013). In our study, scats used to evaluate DNA degra-
dation varied between species in origin and length of
storage. We obtained fresh scats from free-ranging
foxes during live capture, but fresh scats from free-
ranging coyotes were unavailable. Consequently, fresh
coyote scats were obtained from a captive population.
Although scats were frozen upon collection, stored for
variable lengths of time and thawed prior to placement
in the field, we do not feel that storage time or the
freezethaw cycle significantly impacted PCR success.
While we did not explicitly test the influence of freez-
ing during this study, we previously evaluated PCR
success of canid scats stored in a standard freezer and
©2014 John Wiley & Sons Ltd
OPTIMIZING NONINVASIVE GENETIC SAMPLING 9
found no decline in PCR success for samples frozen for
up to 1 year, when the study ended (L. P. Waits &J. R.
Adams, unpublished data). Our results support this
conclusion. On average, fox and coyote scats were
stored frozen 18 months and <1 month, respectively.
Despite the longer storage time of fox scats, observed
PCR success rates were the same (mtDNA) or higher
(nDNA) for foxes in both seasons and scats of both spe-
cies produced high PCR success at the earliest time
points (Fig. S1, Supporting information). Additionally,
winter temperatures at our site fluctuate from below to
above freezing (night vs. day temperatures) and scats
naturally experience repeated freezethaw cycles, yet in
this experiment, we observed higher PCR success rates
for both species in winter relative to summer, suggest-
ing that freezethaw cycles were not the driving cause
of DNA degradation.
Variation in diets between captive and free-ranging
coyotes may also influence the generalization of results
to the wild population. Differences in diet could influ-
ence the rate of intestinal cell shedding or the amount
of inhibitors in faecal samples. However, we do not
believe that using captive coyote scats substantially
influenced our results. We have data on success rates
for free-ranging coyote samples collected in winter
and summer 2013, and results are comparable to
model-predicted results from our degradation experi-
ment. For example, for a 14-day interval our model
predicted mean nDNA success rates for coyote scats
of 64.6% (winter; range 46.580.7%; Fig. 3) and 47.7%
(summer; range 24.971.2%; Fig. 3), and success rates
for free-ranging coyotes sampled with a 14 day inter-
val were 78% (winter) and 55% (summer).
We analysed winter and summer degradation within
the same models for PCR success, ADO and FA to increase
sample size and statistical power, but winter samples were
only available through day 21. Model-predicted results for
winter intervals >21 days assume that trends in predicted
values continue in the same way beyond 21 days (i.e. that
the log odds of the outcome is linear in the log of time), and
consequently, these predictions should be interpreted with
caution. Missing winter data points do not affect the infer-
ences 21 days, and it is during this time that the most sub-
stantial changes occurred (Fig. 3).
Monitoring and management implications
This study presents a conceptual model for optimizing
NDS for capturerecapture analysis, which can be
extended to any species or system where estimates of
sample accumulation (e.g. hair snaring rate, scat accu-
mulation rate) and DNA degradation rates can be
quantified. We demonstrate that this novel optimiza-
tion approach can effectively reduce costs of NDS
monitoring programmes. By initiating a pilot study to
evaluate sample accumulation and DNA degradation
rates, NDS monitoring costs can be minimized, allow-
ing monitoring to occur over larger spatial extents and
at higher temporal resolutions than would be possible
otherwise. Differences observed in sample accumula-
tion and DNA degradation rates between species and
across seasons, at the same study site, reiterate the
importance of pilot studies for effectively implement-
ing NDS (Taberlet et al. 1999; Waits & Paetkau 2005).
We recommend that when possible pilot studies incor-
porating DNA degradation should use fresh scats col-
lected from target populations. Additionally,
practitioners optimizing NDS should compare field
collected data to model-predicted results to evaluate
model performance, particularly, when samples used
during pilot studies have an origin other than the
population being monitored.
Kit fox populations are believed to be declining,
and their contemporary distribution is unclear. High
mtDNA success suggests that NDS can be used to
explore presence or occupancy of elusive species, such
as kit fox, across large spatial areas. When employing
NDS for occupancy modelling (or similar approaches),
researchers should acknowledge that mtDNA amplifi-
cations may incorporate old samples violating closure
assumptions and should clear transects before survey-
ing or evaluate sample persistence (MacKenzie &
Reardon 2013). Nuclear DNA success rates were suffi-
cient to identify individuals and provide an effective
capturerecapture approach to estimate population
demographic parameters (Kohn et al. 1999; Marucco
et al. 2011). Both mtDNA and nDNA can be used for
monitoring communities or intraguild interactions and
provide a cost-effective means to monitor management
strategies.
Acknowledgements
Funding provided by the U.S. Department of Defense Environ-
mental Security Technology Certification Programme (12-EB-
RC5-006), Legacy Resource Management Programme (W9132T-
12-2-0050) and Army DPG Environmental Programme. Addi-
tional funding and logistical assistance provided by the U.S.
Department of Agriculture, Wildlife Services, National Wildlife
Research Center; and the Endangered Species Mitigation Fund
of the Utah Department of Natural Resources, Division of Wild-
life Resources. R Knight was essential to securing funding and
provided logistical support. J Adams assisted with laboratory
procedures.
References
Andelt WF, Andelt SH (1984) Diet bias in scat deposition-rate surveys of
coyote density. Wildlife Society Bulletin,12,7477.
©2014 John Wiley & Sons Ltd
10 R. C. LONSINGER ET AL.
Arjo WM, Gese EM, Bennett TJ, Kozlowski AJ (2007) Changes in kit fox
coyoteprey relationships in the Great Basin Desert, Utah. Western
North American Naturalist,67, 389401.
Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G (2009)
Advancing ecological understandings through technological transfor-
mations in noninvasive genetics. Molecular Ecology Resources,9, 1279
1301.
Broquet T, Petit E (2004) Quantifying genotyping errors in noninvasive
population genetics. Molecular Ecology,13, 36013608.
Brøseth H, Flagstad Ø, W
ardig C, Johansson M, Ellegren H (2010)
Large-scale noninvasive genetic monitoring of wolverines using
scats reveals density dependent adult survival. Biological Conserva-
tion,143, 113120.
Buchan JC, Archie EA, Van Horn RC, Moss CJ, Alberts SC (2005) Locus
effects and sources of error in noninvasive genotyping. Molecular Ecol-
ogy Notes,5, 680683.
De Barba M, Adams JR, Goldberg CS et al. (2014) Molecular species iden-
tification for multiple carnivores. Conservation Genetics Resources,6,
821824.
DeMay SM, Becker PA, Eidson CA et al. (2013) Evaluating DNA degrada-
tion rates in faecal pellets of the endangered pygmy rabbit. Molecular
Ecology Resources,13, 654662.
Dempsey SJ (2013) Evaluation of survey methods and development of
species distribution models for kit foxes in the Great Basin Desert.
M.S. Thesis, Utah State University.
Gese EM (2001) Monitoring of terrestrial carnivore populations. In: Carni-
vore Conservation(eds Gittleman JL, Funk SM, MacDonald D, Wayne
RK), pp. 372396. Cambridge University Press, New York.
Kohn MH, York EC, Kamradt DA et al. (1999) Estimating population size
by genotyping faeces. Proceedings of the Royal Society of London, Series B:
Biological Sciences,266, 657663.
Kozlowski AJ, Gese EM, Arjo WM (2008) Niche overlap and resource par-
titioning between sympatric kit foxes and coyotes in the Great Basin
Desert of western Utah. American Midland Naturalist,160, 191208.
Kozlowski AJ, Gese EM, Arjo WM (2012) Effects of intraguild predation:
evaluating resource competition between two canid species with
apparent niche separation. International Journal of Ecology,2012,112.
Kuhn M, Weston S, Wing J, Forester J (2011) Contrast: A Collection of Con-
trast Methods. R package version 0.17. Available from http://CRAN.
R-project.org/package=contrast.
Livingston TR, Gipson PS, Ballard WB, Sanchez DM, Krausman PR
(2005) Scat removal: a source of bias in feces-related studies. Wildlife
Society Bulletin,33, 172178.
Long RA, Donovan TM, MacKay P, Zielinski WJ, Buzas JS (2011) Predict-
ing carnivore occurrence with noninvasive surveys and occupancy
modeling. Landscape Ecology,26, 327340.
Lukacs PM, Burnham KP (2005) Review of capture-recapture methods
applicable to noninvasive genetic sampling. Molecular Ecology,14,
39093919.
MacKenzie DI, Reardon JT (2013) Occupancy methods for conservation
management. In: Biodiversity Monitoring and Conservation: Bridging
the Gap Between Global Commitment and Local Action(eds Collen B,
Pettorelli N, Bailie JEM, Durant SM), pp. 248264. John Wiley &
Sons, Hoboken.
Marucco F, Boitani L, Pletscher DH, Schwartz MK (2011) Bridging the
gaps between non-invasive genetic sampling and population parame-
ter estimation. European Journal of Wildlife Research,57,113.
Murphy MA, Waits LP, Kendall KC et al. (2002) An evaluation of long-
term preservation methods for brown bear (Ursus arctos) faecal DNA
samples. Conservation Genetics,3, 435440.
Murphy MA, Waits LP, Kendall KC (2003) The influence of diet on faecal
DNA amplification and sex identification in brown bears (Ursus arctos).
Molecular Ecology,12, 22612265.
Murphy MA, Kendall KC, Robinson A, Waits LP (2007) The impact of
time and field conditions on brown bear (Ursus arctos) faecal DNA
amplification. Conservation Genetics,8, 12191224.
Nsubuga AM, Robbins MM, Roeder AD et al. (2004) Factors affecting the
amount of genomic DNA extracted from ape faeces and the identifica-
tion of an improved sample storage method. Molecular Ecology,13,
20892094.
O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods in
Ecology and Evolution,1, 118122.
Onorato D, White C, Zager P, Waits LP (2006) Detection of predator pres-
ence at elk mortality using mtDNA analysis of hair and scat samples.
Wildlife Society Bulletin,34, 815820.
Palomares F, Godoy JA, Piriz A, O’Brien J, Johnson WE (2002) Faecal
genetic analysis to determine the presence and distribution of elu-
sive carnivores: design and feasibility. Molecular Ecology,11, 2171
2182.
Panasci M, Ballard WB, Breck S et al. (2011) Evaluation of fecal DNA
preservation techniques and effects of sample age and diet on geno-
typing success. Journal of Wildlife Management,75, 16161624.
Piggott MP (2004) Effect of sample age and season of collection on the
reliability of microsatellite genotyping of faecal DNA. Wildlife Research,
31, 485.
Piggott MP, Taylor AC (2003) Extensive evaluation of faecal preservation
and DNA extraction methods in Australian native and introduced spe-
cies. Australian Journal of Zoology,51, 341.
Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors:
causes, consequences and solutions. Nature Reviews Genetics,6, 847
859.
Prugh LR, Ritland CE, Arthur SM, Krebs CJ (2005) Monitoring coyote
population dynamics by genotyping faeces. Molecular Ecology,14,
15851596.
R Core Team (2014) R: A Language and Environment for Statistical Comput-
ing. R Foundation for Statistical Computing, Vienna. URL http://
www.R-project.org/.
Ralls K, Sharma S, Smith DA et al. (2010) Changes in kit fox defecation
patterns during the reproductive season: implications for noninvasive
surveys. Journal of Wildlife Management,74, 14571462.
Santini A, Lucchini V, Fabbri E, Randi E (2007) Ageing and environmen-
tal factors affect PCR success in wolf (Canis lupus) excremental DNA
samples. Molecular Ecology Notes,7, 955961.
SAS Institute Inc. (2011) SAS/STAT
â
9.3 User’s Guide. SAS Institute Inc.,
Cary.
Scandura M, Capitani C, Iacolina L, Marco A (2006) An empirical
approach for reliable microsatellite genotyping of wolf DNA from
multiple noninvasive sources. Conservation Genetics,7, 813823.
Schauster ER, Gese EM, Kitchen AM (2002) An evaluation of survey
methods for monitoring swift fox abundance. Wildlife Society Bulletin,
30, 464477.
Seutin G, White BN, Boag PT (1991) Preservation of avian blood and
tissue samples for DNA analyses. Canadian Journal of Zoology,69,
8290.
Solberg KH, Bellemain E, Drageset O-M, Taberlet P, Swenson JE (2006)
An evaluation of field and non-invasive genetic methods to estimate
brown bear (Ursus arctos) population size. Biological Conservation,128,
158168.
Stenglein JL, DE Barba M, Ausband DE, Waits LP (2010a) Impacts of
sampling location within a faeces on DNA quality in two carnivore
species. Molecular Ecology Resources,10, 109114.
Stenglein JL, Waits LP, Ausband DE, Zager P, Mack CM (2010b) Efficient,
noninvasive genetic sampling for monitoring reintroduced wolves.
Journal of Wildlife Management,74, 10501058.
Taberlet P, Luikart G (1999) Non-invasive genetic sampling and indi-
vidual identification. Biological Journal of the Linnean Society,68,41
55.
Taberlet P, Waits LP, Luikart G (1999) Noninvasive genetic sam-
pling: look before you leap. Trends in Ecology and Evolution,14,
323327.
Waits LP, Paetkau D (2005) Noninvasive genetic sampling tools for
wildlife biologists: a review of applications and recommendations
for accurate data collection. Journal of Wildlife Management,69,
14191433.
Williams BK, Nichols JD, Conroy MJ (2002) Analysis and Management of
Animal Populations. Academic Press, San Diego.
©2014 John Wiley & Sons Ltd
OPTIMIZING NONINVASIVE GENETIC SAMPLING 11
Woodruff SP, Johnson TR, Waits LP (in press) Evaluating the interaction
of faecal pellet deposition rates and DNA degradation rates to maxi-
mize sampling design for DNA-based mark-recapture analysis of Son-
oran pronghorn. Molecular Ecolog Resources.
R.C.L. performed data collection, laboratory procedures,
data analysis and interpretation and wrote the manu-
script. E.M.G., S.J.D. and B.M.K. provided scats for DNA
degradation experiments and assisted with data collec-
tion. T.R.J. assisted with statistical analyses and interpre-
tation. L.P.W. designed the study and assisted with data
interpretation. All authors assisted with the manuscript
preparation.
Data accessibility
Raw data (.csv) and analysis code for scat accumulation
(R script) and models of PCR success, ADO and FA (SAS
scripts) are available on Dryad, doi:10.5061/dryad.23k27.
Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Fig. S1 Observed per cent PCR success for mitochondrial
(mtDNA) and nuclear (nDNA) DNA for kit fox (Vulpes macrotis)
and coyote (Canis latrans) faecal DNA samples.
Fig. S2 Observed nuclear DNA genotyping error rates (i.e. allelic
dropout and false alleles) for kit fox (Vulpes macrotis) and coyote
(Canis latrans) faecal DNA samples.
Fig. S3 Proportion of samples accumulated for kit fox (Vulpes
macrotis) and coyote (Canis latrans) in winter and summer that
were predicted to fail for individual identification across sam-
pling intervals.
Appendix S1PCR conditions, including primer concentrations and
thermal profiles, for mitochondrial and nuclear DNA amplification.
©2014 John Wiley & Sons Ltd
12 R. C. LONSINGER ET AL.
... Third, research using fecal DNA should focus on maximizing the success of laboratory analyses, such as reducing the amount of time between deposition and collection as much as possible and collecting a large number of samples [95]. This was not possible in the national park setting where we conducted this project, and thus scats were collected, on average, 17 days following deposit (range 1-75 days). ...
... Despite this, we had moderate genotyping success at 45% (49/110). In agreement with other studies, we found that DNA quality was better retained in the winter, at dens, when scats are protected from solar radiation, and when scats are collected with time minimized between deposition and collection in the field [17,95,96]. The climate in Yellowstone National Park and other northern temperate regions is therefore moderately favorable year-round and ideal in the winter for noninvasive molecular research, suggesting that samples must be collected sooner after deposition in wetter, warmer climates to achieve similar genotyping success. ...
... The climate in Yellowstone National Park and other northern temperate regions is therefore moderately favorable year-round and ideal in the winter for noninvasive molecular research, suggesting that samples must be collected sooner after deposition in wetter, warmer climates to achieve similar genotyping success. Without the sampling design limitations of our project [95], these methods could be a powerful approach for assessing noninvasive parasite infection patterns in a population of terrestrial mammals. ...
Article
Full-text available
Helminth infections are cryptic and can be difficult to study in wildlife species. Helminth research in wildlife hosts has historically required invasive animal handling and necropsy, while results from noninvasive parasite research, like scat analysis, may not be possible at the helminth species or individual host levels. To increase the utility of noninvasive sampling , individual hosts can be identified by applying molecular methods. This allows for longitudinal sampling of known hosts and can be paired with individual-level covariates. Here we evaluate a combination of methods and existing long-term monitoring data to identify patterns of cestode infections in gray wolves in Yellowstone National Park. Our goals were: (1) Identify the species and apparent prevalence of cestodes infecting Yellowstone wolves; (2) Assess the relationships between wolf biological and social characteristics and cestode infections; (3) Examine how wolf samples were affected by environmental conditions with respect to the success of individual genotyping. We collected over 200 wolf scats from 2018-2020 and conducted laboratory analyses including individual wolf genotyping, sex identification, cestode identification, and fecal glucocorticoid measurements. Wolf genotyp-ing success rate was 45%, which was higher in the winter but decreased with higher precipitation and as more time elapsed between scat deposit and collection. One cestode species was detected in 28% of all fecal samples, and 38% of known individuals. The most common infection was Echinococcus granulosus sensu lato (primarily E. canadensis). Adult wolves had 4x greater odds of having a cestode infection than pups, as well as wolves sampled in the winter. Our methods provide an alternative approach to estimate cestode prevalence and to linking parasites to known individuals in a wild host system, but may be most useful when employed in existing study systems and when field collections are designed to minimize the time between fecal deposition and collection.
... We selected this season for comparative analyses for multiple reasons. First, managers at Dugway are more likely to conduct annual monitoring during summer due to higher sample accumulation rates (Lonsinger et al. 2015a), easier access (i.e., relative to winter), the cost-effectiveness of hiring summer technicians, and a goal of understanding how military training activities that peak around June-July influence kit fox occurrence. Furthermore, samples collected in 2013 were used to develop the statistical classification trees (Lonsinger et al. 2015b) employed as one sample identification criterion, and we wanted to test this criterion on an independent data set. ...
... We performed DNA extraction in a laboratory dedicated to low-quality samples. Our DNA extraction and amplification procedures are detailed in Lonsinger et al. (2015a). ...
... Our sample prioritization scheme (i.e., the freshest scats believed to be kit fox based on ambiguous criteria) limited the number of genetic tests required to detect the target species, without reducing the data necessary to characterize detection, as we observed with the genetic removal design. The conditional-replicate design required additional information on relative freshness of scats, which influences DNA amplification rates (Lonsinger et al. 2015a). Analogous procedures could be extended to the application of machine learning for camera-based monitoring. ...
Article
Optimization of occupancy‐based monitoring has focused on balancing the number of sites and surveys to minimize field efforts and costs. When survey techniques require post‐field processing of samples to confirm species detections, there may be opportunities to further improve efficiency. We used scat‐based noninvasive genetic sampling for kit foxes (Vulpes macrotis) in Utah, USA, as a model system to assess post‐field data processing strategies, evaluate the impacts of these strategies on estimates of occupancy and associations between parameters and predictors, and identify the most cost‐effective approach. We identified scats with three criteria that varied in costs and reliability: (i) field‐based identification (expert opinion), (ii) statistical‐based morphological identification, and (iii) genetic‐based identification (mitochondrial DNA). We also considered four novel post‐field sample processing strategies that integrated statistical and genetic identifications to reduce costly genetic procedures, including (iv) a combined statistical‐genetic identification, (v) a genetic removal design, (vi) a within‐survey conditional‐replicate design, and (vii) a single‐genetic‐replicate with false‐positive modeling design. We considered results based on genetic identification as the best approximation of truth and used this to evaluate the performance of alternatives. Field‐based and statistical‐based criteria prone to misidentification produced estimates of occupancy that were biased high (~1.8 and 2.1 times higher than estimates without misidentifications, respectively). These criteria failed to recover associations between parameters and predictors consistent with genetic identification. The genetic removal design performed poorly, with limited detections leading to estimates that were biased high with poor precision and patterns inconsistent with genetic identification. Both statistical‐genetic identification and the conditional‐replicate design produced occupancy estimates comparable to genetic identification, while recovering the same model structure and associations at cost reductions of 67% and 74%, respectively. The false‐positive design had the lowest cost (88% reduction) and recovered patterns consistent with genetic identification but had occupancy estimates that were ~32% lower than estimated occupancy based on genetic identification. Our results demonstrate that careful consideration of detection criteria and post‐field data processing can reduce costs without significantly altering resulting inferences. Combined with earlier guidance on sampling designs for occupancy modeling, these findings can aid managers in optimizing occupancy‐based monitoring.
... Microsatellite markers for Black Vultures and Turkey Vultures (Cathartes aura) were developed by Wostenberg et al. (2019) that could identify the 2 New World species and provide individual identification for populationlevel studies. However, before noninvasive genetic sampling can be employed at large spatial extents under field settings, pilot studies that determine DNA amplification success are essential (Taberlet et al. 1999, Lonsinger et al. 2015. ...
... Thus, our 12 h collection time recommendation may be very conservative. As with all noninvasive studies it is best to trial a pilot study in a target study area to determine sitespecific DNA persistence and collection challenges (Lonsinger et al. 2015). ...
Article
Full-text available
Studies that rely on noninvasive collection of DNA for birds often use feces or feathers. Some birds, such as vultures, regurgitate undigested matter in the form of pellets that are commonly found under roost sites. Our research demonstrates that regurgitated pellets are a viable, noninvasive source of DNA for molecular ecology studies of vultures. Our objectives were to amplify 5 microsatellite loci designed for distinguishing Turkey Vultures (Cathartes aura) and Black Vultures (Coragyps atratus) in a single, multiplexed PCR, and to determine how long the target nuclear DNA persists after a vulture pellet is regurgitated and exposed to the environment. We collected pellets from captive Black Vultures and placed them in an outdoor aviary for a maximum estimated total of 12, 24, 36, or 48 h. We swabbed pellet surfaces for extraction and amplified vulture DNA using the panel of markers. All amplified alleles fell within predicted ranges of Black Vultures for all 5 loci, supporting the use of this microsatellite panel for vulture species identification. Overall amplification success for samples collected 0–12 h after regurgitation was 82.3%. Pellets collected 12–24 h, 24–36 h, and 36–48 h after regurgitation had only 18%, 10.2%, and 4.5% amplification success, respectively, which may have been due to a rain event. Our approach will be useful for noninvasive genetic sampling targeting nuclear DNA. These results should encourage noninvasive genetic sampling studies of other species that regurgitate pellets, such as raptors, water birds, or shorebirds.
... That we found a greater number of Coyotes in the urban study area and also collected greater numbers of Coyote scats suggests Coyote density may be higher in Jacksonville than at MAERC, but more robust data are needed to reliably state whether Coyote densities differed between rural and urban study areas. The estimated Coyote scat accumulation rate (0.02 scats/km/day) was lower than reported elsewhere (0.076 scats/km/day; Lonsinger et al. 2015). These results may be due to lower detection probability than experienced by Lonsinger et al. (2015), who conducted their study in xeric, resource poor environments where scat detection probability may have been higher due to less vegetation on transects. ...
... The estimated Coyote scat accumulation rate (0.02 scats/km/day) was lower than reported elsewhere (0.076 scats/km/day; Lonsinger et al. 2015). These results may be due to lower detection probability than experienced by Lonsinger et al. (2015), who conducted their study in xeric, resource poor environments where scat detection probability may have been higher due to less vegetation on transects. An important finding to consider with regards to our reported scat accumulation rates is that the number of scats observed was highly disproportionate (non-uniform) across transects. ...
Article
Full-text available
Coyotes (Canis latrans) are expanding their range and due to conflicts with the public and concerns of Coyotes affecting natural resources such as game or sensitive species, there is interest and often a demand to monitor Coyote populations. A challenge to monitoring is that traditional invasive methods involving live-capture of individual animals are costly and can be controversial. Natural resource management agencies can benefit from contemporary noninvasive genetic sampling approaches aimed at determining key aspects of Coyote ecology (e.g., population density and food habits). However, the efficacy of such approaches under different environmental conditions is poorly understood. Our objectives were to 1) examine accumulation and nuclear DNA degradation rates of Coyote scats in metropolitan and rural sites in Florida to help optimize methods to estimate population density; and 2) explore new genetic methods for determining diet of Coyotes based on vertebrate, plant, and invertebrate species DNA identified in scat. Recently developed DNA metabarcoding approaches make it possible to simultaneously identify DNA from multiple prey species in predator scat samples, but an exploration of this tool for assessing Coyote diet has not been pursued. We observed that scat accumulation rates (0.02 scats/km/day) did not vary between sites and fecal DNA amplification success decreased and genotyping errors increased over time with exposure to sun and precipitation. DNA sampling allowed us to generate a Coyote density estimate for the urban environment of eight Coyotes per 100 km2, but lack of recaptures in the rural area precluded density estimation. DNA metabarcoding showed promise for assessing diet contributions of vertebrate species to Coyote diet. Feral Swine (Sus scrofa) were detected as prey at higher frequencies than previously reported. We identify several considerations that can be used to optimize future noninvasive sampling efforts for Coyotes in the southeastern United States. We also discuss strengths and drawbacks of utilizing DNA metabarcoding for assessing diet of generalist carnivores such as Coyotes. .
... Dloniak et al. (2004) also found stability in fecal GCM concentrations over 48 hours in scat of spotted hyena (Crocuta crocuta). In contrast, Muehlenbein et al. (2012) found an increase in variability of the concentration levels within 3 hours after defecation in orangutans (Pongo pygmaeus morio), while Mӧstl et al. (1999) Schauster et al. 2002;Dempsey et al. 2014;Lonsinger et al. 2015Lonsinger et al. , 2018. Therefore, determining the amount of time that fecal GCM concentrations persist consistently in scats could assist in determining when scat collections should occur, thereby increasing the efficacy and reducing the costs of canid scat collections. ...
... Determining the rate of degradation has been conducted for canid fecal DNA (Lonsinger et al. 2015), and we strongly recommend the same for fecal GCMs, both across canid species and across taxa. Other studies have reported changes when fecal samples were not frozen immediately. ...
Article
Studying physiologic stress responses can assist in understanding the welfare of animals. One method of measuring the physiologic stress response is evaluating concentrations of glucocorticoid metabolites in feces. Previously, using an adrenocorticotropic hormone challenge, we found fecal glucocorticoid metabolite levels were a reliable indicator of physiologic stress response in coyotes (Canis latrans). We determine whether glucocorticoid metabolite concentrations remain stable when collecting feces over a 2-week period, a timeframe commonly used in scat surveys for wild canids. We collected feces from 6 captive coyotes maintained at the U.S. Department of Agriculture, Wildlife Services, National Wildlife Research Center, Predator Research Facility near Millville, Utah, USA, and exposed them to the environment for 13 days during summer (August 26 to September 8, 2011) and winter (January 11–24, 2012). Every 2 days, we collected a sub-sample from each individual scat and then quantified the concentration of fecal glucocorticoid metabolites. We found changes in fecal glucocorticoid metabolite concentrations over the 13-day period, with values increasing 45–79% from day 1 to day 3 of sampling. There was also high variation in fecal glucocorticoid metabolites among individuals over time. We provide evidence that fecal samples collected in the field even 3 days after defecation will not provide reliable measures of fecal glucocorticoid metabolites and thus recommend using only fresh fecal samples. We also recommend that, due to high individual variability in fecal glucocorticoid metabolites, a large number of individuals be sampled when a population-wide assessment is desired.
... sample size needs, sampling design, costs, and required effort for individual needs, as has been done for faecal mark-recapture studies (Lonsinger et al., 2015). Overall, consultation with a lab experienced in eDNA studies is ideal, and there are useful resources for those wanting to begin the process of using eDNA 1 . ...
Article
Full-text available
Biodiversity must be documented before it can be conserved. However, it may be difficult to document species with few individuals (Thompson, 2013; Goldberg et al., 2016), thus it requires a multitude of tools to detect species that occur in low numbers or are elusive (see the various chapters in this volume). One tool that has become useful for conservation efforts utilizes environmental DNA, which is DNA shed into the environment by organisms (eDNA; Taberlet et al., 2018). Typically this involves taking environmental samples such as soil, water, air, or using biological surrogates for sampling biodiversity (e.g. leeches, sponges, carrion flies, etc.; Schnell et al., 2012; Calvignac-Spencer et al., 2013; Lynggaard et al., 2019; Mariani et al., 2019) and using laboratory approaches to concentrate, isolate, and test for target DNA through polymerase chain reaction (PCR) amplification (Taberlet et al., 2018). The utilization of eDNA for species detection is part of a larger field of non-invasive DNA sampling, which more broadly includes collecting DNA passively from wildlife, through collection of faeces, saliva, feathers, hair, or other methods of sampling shed DNA. Environmental DNA has been used to document presence/absence of a target species (Ficetola et al., 2008a, 2008b; Himter et al., 2017) or to quantify relative abundance for biodiversity from varied environments such as the arctic (e.g. Leduc et al., 2019; Von Duyke et al., 2019), marine (e.g. Port et al., 2016; Jo et al, 2017; Stoeckle et al., 2018), freshwater (e.g. Lacoursi^re-Roussel et al., 2016; Doi et al., 2017), and tropical (e.g. Schnell et al., 2012; Gogarten et al, 2020) ecosystems. The application of this technology includes the detection of invasive species, pathogens (including DNA and RNA), species of conservation concern, and biodiversity (Acevedo-Whitehouse et al., 2010; Rees et al., 2014; Sakai et al, 2019). In this world -of 'fast-paced technological advances, not all new methods prove useful in an applied context. Although eDNA has not been used regularly in biodiversity conservation for more than a decade, it has proven to be an extremely practical and informative tool. The utility of eDNA is supported by ongoing advancements and development of novel applications. There is no easy way to standardize the application or methods of eDNA as the conservation question, and the target system must drive the selection of a range of options at every step. However, guidelines now exist for the best practices of optimizing a sampling scheme and sample processing for eDNA applications (Goldberg et al., 2016; Jeunen et al., 2019; Klymus et al., 2020; The eDNA Society, 2019; Shu et al, 2020). Further, the ranks of experienced eDNA practitioners have expanded globally; thus, it is fairly easy to find expert consultation. Therefore, it is now practical and prudent to adopt eDNA in the service of biodiversity conservation efforts.
... sample size needs, sampling design, costs, and required effort for individual needs, as has been done for faecal mark-recapture studies (Lonsinger et al., 2015). Overall, consultation with a lab experienced in eDNA studies is ideal, and there are useful resources for those wanting to begin the process of using eDNA 1 . ...
Chapter
Detection and monitoring of wildlife species of concern is a costly and time-consuming challenge that is critical to the management of such species. Tools such as lures and traps can cause unnecessary stress or other health impacts to sensitive species. Development and refinement of tools that provide means to detect rare and elusive species without requiring contact with them reduce such impacts. Further, the potential of detection after the target species has moved on from a sampling site could allow for higher potential for detection of rare species. The ability to amplify DNA from environmental samples (e.g. water, soil, air, and other substrates) has provided a non-invasive method for detection of rare or elusive species while reducing negative impacts to wildlife. Like other non-invasive methods, such as cameras, there are methodological pitfalls associated with environmental DNA (eDNA) sampling to consider. Each study system will provide unique challenges to adequate eDNA sampling. Thus, pilot studies are critical for successful implementation of a larger-scale detection and monitoring study. This chapter will describe the benefits and challenges of using eDNA, detail types of eDNA sampling, and provide guidance on designing appropriate study design and sampling schemes. Empirical studies using eDNA applied to wildlife conservation efforts will be highlighted and discussed.
... As noted for both scat and hair samples collected from other carnivores in the southwestern United States (Gould et al., 2018;Naidu et al., 2011), we suspect that high ultraviolet radiation in the region caused rapid scat decomposition and DNA degradation (Pilliod et al., 2014;Strickler et al., 2015). Survey occasions <7 days in duration may be necessary to combat conditions present in the Southwest; although scat accumulation rates at latrines tend to be slow for otters (Gallant et al., 2007;Rivera et al., 2019), so shorter occasion durations may result in fewer samples collected (Lonsinger et al., 2015). Additionally, recently developed alternative fecal DNA sampling methods, such as swabbing a scat with a cotton swab rinsed in DNA lysis buffer, may improve genotyping success rates for otters (Klütsch & Thomas, 2018); however, the swabbing method had inferior genotyping success rates for carnivore fecal samples collected in arid environments that were similar to our study area (Miles et al., 2015). ...
Article
Full-text available
Monitoring the demographics and genetics of reintroduced populations is critical to evaluating reintroduction success, but species ecology and the landscapes that they inhabit often present challenges for accurate assessments. If suitable habitats are restricted to hierarchical dendritic networks, such as river systems, animal movements are typically constrained and may violate assumptions of methods commonly used to estimate demographic parameters. Using genetic detection data collected via fecal sampling at latrines, we demonstrate applicability of the spatial capture–recapture (SCR) network distance function for estimating the size and density of a recently reintroduced North American river otter (Lontra canadensis) population in the Upper Rio Grande River dendritic network in the southwestern United States, and we also evaluated the genetic outcomes of using a small founder group (n = 33 otters) for reintroduction. Estimated population density was 0.23–0.28 otter/km, or 1 otter/3.57–4.35 km, with weak evidence of density increasing with northerly latitude (β = 0.33). Estimated population size was 83–104 total otters in 359 km of riverine dendritic network, which corresponded to average annual exponential population growth of 1.12–1.15/year since reintroduction. Growth was ≥40% lower than most reintroduced river otter populations and strong evidence of a founder effect existed 8–10 years post‐reintroduction, including 13–21% genetic diversity loss, 84%–87% genetic effective population size decline, and rapid divergence from the source population (FST accumulation = 0.06/generation). Consequently, genetic restoration via translocation of additional otters from other populations may be necessary to mitigate deleterious genetic effects in this small, isolated population. Combined with non‐invasive genetic sampling, the SCR network distance approach is likely widely applicable to demogenetic assessments of both reintroduced and established populations of multiple mustelid species that inhabit aquatic dendritic networks, many of which are regionally or globally imperiled and may warrant reintroduction or augmentation efforts. Monitoring the demographics and genetics of reintroduced populations is critical to evaluating reintroduction success, but species ecology and the landscapes that they inhabit often present challenges for accurate assessments. Using genetic detection data collected via fecal sampling at latrines, we demonstrate applicability of the spatial capture–recapture (SCR) network distance function for estimating the size and density of a recently reintroduced North American river otter (Lontra canadensis) population in the Upper Rio Grande River dendritic network in the southwestern United States, and we also evaluated the genetic outcomes of using a small founder group for reintroduction. Combined with noninvasive genetic sampling, the SCR network distance approach is likely widely applicable to demogenetic assessments of both reintroduced and established populations of multiple mustelid species that inhabit aquatic dendritic networks, many of which are regionally or globally imperiled and may warrant reintroduction or augmentation efforts.
... This is, in part, because it is important to maximize sampling opportunities for elusive species, given the labor-intensive nature of field work, but also because certain parameters (e.g., genotype capture-recapture methods to estimate census size) require the application of assumptions about sampling that may or may not be satisfied if sampling is conducted incorrectly. It is also important to consider the temporal sampling interval which can affect sample sizes, genotyping success rates, genotyping error rates and impact the ability to meet modeling assumptions for mark-recapture and occupancy analyses (Lonsinger et al., 2015;Woodruff et al., 2015). ...
Preprint
Full-text available
Emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD-seq) require large amounts of high quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g. collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically-driven declines, which are unlikely to abate without intensive management efforts that often include MIS approaches. Here we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy datasets and recommend how to address the challenges of moving between traditional and next generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.
... This is, in part, because it is important to maximize sampling opportunities for elusive species, given the labor-intensive nature of field work, but also because certain parameters (e.g., genotype capture-recapture methods to estimate census size) require the application of assumptions about sampling that may or may not be satisfied if sampling is conducted incorrectly. It is also important to consider the temporal sampling interval which can affect sample sizes, genotyping success rates, genotyping error rates and impact the ability to meet modeling assumptions for mark-recapture and occupancy analyses (Lonsinger et al., 2015;Woodruff et al., 2015). ...
Preprint
Full-text available
Emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD-seq) require large amounts of high quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g. collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically-driven declines, which are unlikely to abate without intensive management efforts that often include MIS approaches. Here we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy datasets and recommend how to address the challenges of moving between traditional and next generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.
Article
Full-text available
The use of noninvasively collected samples greatly expands the range of ecological issues that may be investigated through population genetics. Furthermore, the difficulty of obtaining reliable genotypes with samples containing low quantities of amplifiable DNA may be overcome by designing optimal genotyping schemes. Such protocols are mainly determined by the rates of genotyping errors caused by false alleles and allelic dropouts. These errors may not be avoided through laboratory procedure and hence must be quantified. However, the definition of genotyping error rates remains elusive and various estimation methods have been reported in the literature. In this paper we proposed accurate codification for the frequencies of false alleles and allelic dropouts. We then reviewed other estimation methods employed in hair- or faeces-based population genetics studies and modelled the bias associated with erroneous methods. It is emphasized that error rates may be substantially underestimated when using an erroneous approach. Genotyping error rates may be important determinants of the outcome of noninvasive studies and hence should be carefully computed and reported.
Article
Full-text available
Knowledge of population demographics is important for species management but can be challenging in low-density, wide-ranging species. Population monitoring of the endangered Sonoran pronghorn (Antilocapra americana sonoriensis) is critical for assessing the success of recovery efforts, and noninvasive DNA sampling (NDS) could be more cost-effective and less intrusive than traditional methods. We evaluated faecal pellet deposition rates and faecal DNA degradation rates to maximize sampling efficiency for DNA-based mark-recapture analyses. Deposition data was collected at five watering holes using sampling intervals of one to seven days and averaged one pellet pile per pronghorn per day. To evaluate nuclear DNA (nDNA) degradation, 20 faecal samples were exposed to local environmental conditions and sampled at eight time points from one to 124 days. Average amplification success rates for six nDNA microsatellite loci were 81% for samples on day one, 63% by day seven, 2% by day 14 and 0% by day 60. We evaluated the efficiency of different sampling intervals (1-10 days) by estimating the number of successful samples, success rate of individual identification, and laboratory costs per successful sample. Cost per successful sample increased and success and efficiency declined as the sampling interval increased. Results indicate NDS of faecal pellets is a feasible method for individual identification, population estimation, and demographic monitoring of Sonoran pronghorn. We recommend collecting samples less than seven days old and estimate that a sampling interval of four to seven days in summer conditions (i.e., extreme heat and exposure to UV light) will achieve desired sample sizes for mark-recapture analysis while also maximizing efficiency. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Article
Full-text available
Many studies determine which habitat components are important to animals and the extent their use may overlap with competitive species. However, such studies are often undertaken after populations are in decline or under interspecific stress. Since habitat selection is not independent of interspecific stress, quantifying an animal's current landscape use could be misleading if the species distribution is suboptimal. We present an alternative approach by modeling the predicted distributions of two sympatric species on the landscape using dietary preferences and prey distribution. We compared the observed habitat use of kit foxes ( Vulpes macrotis ) and coyotes ( Canis latrans ) against their predicted distribution. Data included locations of kit foxes and coyotes, carnivore scat transects, and seasonal prey surveys. Although habitats demonstrated heterogeneity with respect to prey resources, only coyotes showed habitat use designed to maximize access to prey. In contrast, kit foxes used habitats which did not align closely with prey resources. Instead, habitat use by kit foxes represented spatial and behavioral strategies designed to minimize spatial overlap with coyotes while maximizing access to resources. Data on the distribution of prey, their dietary importance, and the species-specific disparities between predicted and observed habitat distributions supports a mechanism by which kit fox distribution is derived from intense competitive interactions with coyotes.
Article
Full-text available
Canis latrans diets were summarized by percentage frequency of occurrence of major prey items found in the scats. Coyote scat deposition rates were analyzed relative to 3 prey groups (vertebrates, fruits, and insects). - from Authors
Article
The identification of carnivores responsible for preying on wild or domestic ungulates often is of interest to wildlife managers. Typically, field personnel collect a variety of data at mortality sites including scat or hair samples that may have been deposited by the predator. We compared mitochondrial DNA (mtDNA) analysis of hair and scat samples (n = 122) collected at elk (Cervus elaphus) mortality sites between 1997 and 2004 in north‐central Idaho, USA, with field identification of carnivore presence. We amplified mtDNA from samples via a 2‐step process involving an initial screening for American black bears (Ursus americanus), brown bears (Ursus arctos), and gray wolves (Canis lupus) using a length variation in the 5′ hypervariable section of the control region. Samples that failed the first screening subsequently were analyzed using conserved mtDNA primers that amplify a wide array of vertebrates. Species identification success rate was high (88.5%) and established the presence of 3 predators at elk mortality sites including black bears (55.7%), cougars (Puma concolor; 27.9%), and coyotes (Canis latrans; 6.6%). Attempts at hair and scat identification by field personnel were correct for 58% of hair samples and 79% of fecal samples. Results from these analyses demonstrate the merits of combining field mortality assessments with mtDNA species identification to aid wildlife managers in more accurately pinpointing predators involved in either predation or depredation events.
Article
Species identification is crucial for carnivore conservation and ecological studies. We present a simple molecular genetic test that amplifies DNA of 16 wild carnivore species from three continents. The test is based on co-amplification of two mitochondrial DNA fragments and scoring of the resulting species-specific size patterns. We evaluated the performance of this method using 332 known tissue, blood, hair and fecal samples from 23 carnivore and 11 potential prey species. Results demonstrate that this test can distinguish many Caniform species but not members of Felidae. The test can be performed with a single PCR and capillary sequencer run for cost-effective processing of large sample numbers typical of non-invasive genetic projects.
Article
Consumption of feces (coprophagy) may alter findings of dietary studies and population estimates based on fecal analyses, but its magnitude is poorly understood. We investigated seasonal incidence of scat removal on Fort Riley, Kansas, from January through December 2000. We placed feces from captive bobcats (Lynx rufus), captive coyotes (Canis latrans), and free-ranging coyotes randomly on tracking stations in forest and prairie landscapes to determine rates of scat removal by local wildlife. Rates of removal of feces from captive bobcats, captive coyotes, and free-ranging coyotes varied from 7% during spring to 50% during summer. We identified opossums (Didelphis virginiana) as the most common species present at stations where scat removal occurred. Feces may be an important seasonal source of food for opossums and may provide seasonal dietary supplements for other species. Other factors responsible for disturbance of feces included a woodrat (Neotoma floridana) caching coyote feces, removal of captive coyote feces by free-ranging coyotes accompanied by deposition of fresh feces, a bobcat burying a captive bobcat sample and depositing fresh feces, and rain storms. Dietary studies based on fecal analyses could be biased by scat removal, assuming that contents in feces are representative of the proportion of foods consumed.
Article
Swift foxes (Vulpes velox) were historically distributed across the shortgrass and mixed-grass prairie regions of North America. Today, the swift fox is found in small, isolated populations in the southern and western margins of its historic range. Although methods for censusing wild canids exist, an evaluation of survey techniques for monitoring trends in the abundance of swift foxes has not been conducted. We conducted a 2-year study evaluating 6 survey methods and their ability to accurately monitor changes in swift fox density (independently determined from radiocollared foxes). The study was conducted on the United States Army Piñon Canyon Maneuver Site, southeastern Colorado, from January 1997 to December 1998. We evaluated catch-per-unit-effort (trapping surveys), mark-recapture estimates, scent-post surveys, spotlight counts, scat deposition rate surveys, and an activity index. All surveys were conducted along 5 10-km transects during 3 seasons annually. All methods, except spotlight counts, were reliable and consistent for detecting swift fox presence across the 5 survey transects. Regression analyses indicated that the correlation between swift fox density and survey method varied among methods and seasons, with mark-recapture estimates being the highest predictor (r=0.711), followed by scat deposition surveys (r=0.697), scent-post surveys (r=0.608), spotlight surveys (r=0.420), trapping surveys (r=0.326), and the activity index (r=0.067). Stepwise regression analysis of all survey methods indicated that the combination of mark-recapture estimates and scent-station indices was the highest predictor of swift fox density (r=0.853). The combination of these 2 surveys would be economical and reliable for monitoring swift fox population trends. The combination of scent-station indices and scat deposition surveys was almost as reliable (r=0.829) but was far less costly than surveys involving mark-recapture estimates. A combination of more surveys did little to increase the level of prediction. Survey costs varied due to differing requirements of labor and equipment.