\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{The proof is in the poop: First density estimates for a recovering
bobcat population in southeast Ohio using DNA from scat}



\author[1]{Marissa Dyck}%
\author[2]{Genelle Uhrig}%
\author[2]{Stephen Spear}%
\author[3]{Ruben Iosif}%
\author[1]{Viorel Popescu}%
\affil[1]{Ohio University}%
\affil[2]{The Wilds}%
\affil[3]{Foundation Conservation Carpathia}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The recovery of mammalian species in the US Midwest through natural
recolonization constitutes a conservation success story, yet management
remains challenging due to many unknowns related to population dynamics
and abundance. Abundance is a critical parameter for management
decisions, and estimating the density and abundance of elusive species,
such as terrestrial carnivores, remains challenging despite recent
technological advances. In this study, we evaluated density and
abundance of a recovering carnivore species, the bobcat (Lynx rufus) in
two areas of Ohio using non-invasive DNA from scat. The target areas in
eastern and southern Ohio have been shown to have uneven dynamics and
recolonization success and we expected that this would be reflected in
differences in density and abundance. We collected 298 bobcat scats
between July 2018 and April 2019 on 150 km of repeated transects. Of
these, 102 scats were successfully genotyped, and 55 individuals were
identified (33 in eastern Ohio and 22 in southern Ohio). Using Spatially
Explicit Capture-Recapture models, we estimated 17.9 \selectlanguage{ngerman}± 4.3 and 11.3 ±
2.9 bobcats/100 km2 in eastern and southern Ohio study areas,
respectively. Our results support prior telemetry data which indicated
that bobcats in eastern Ohio had smaller home-ranges than bobcats in
southern Ohio, and thus could support a higher density of individuals.
The higher densities were similar to other eastern US populations and
are much higher than other Midwestern recovering populations. Our
results provide a snapshot of the population status and can be used to
determine sustainable management strategies for Ohio's bobcat population%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Introduction}

Anthropogenic effects on wildlife are ubiquitous and increasing (Dirzo
et al. 2014; Venter et al. 2016; Ibisch et al. 2016; Ceballos et al.
2017). Carnivores are particularly susceptible to human disturbance, due
to their large home range requirements and sensitivity to habitat
fragmentation (Palomares and Caro 1999; Crooks 2002). In recent history
many carnivore ranges have been reduced and/or fragmented due to
colonization and human expansion (Crooks 2002). The bobcat (\emph{Lynx
rufus} ) is among these carnivores; native to North America, bobcats are
a medium-sized felid that occur from southern Canada to central Mexico,
and historically spanned all 48 contiguous U.S. states (Reding et al.
2012). However, in the mid 1800's bobcats were extirpated from a number
of states along the Northeast and the Midwest, including Ohio, due to
deforestation and overharvesting (Reding et al. 2012). In recent years,
bobcat populations have begun to recover in many of these states (Deems
and Pursley 1978). In 1946 an adult male killed along the Ohio River in
Scioto was the first record of a bobcat in Ohio after a century of
extirpation (ODNR 2018), and the number of confirmed bobcat sightings
has been steadily increasing since (Bobcat Management Plan 2023). Recent
evidence indicates that bobcats have successfully recolonized Ohio and
are expanding their range (Roberts and Crimmins 2010; ODNR 2018; Prange
and Rose 2020; Popescu et al. 2021), prompting the Ohio Department of
Natural Resources (ODNR) to remove them from the Ohio Endangered and
Threatened Species List. Consequently, there is increased interest from
recreational hunters and trappers to open a harvest season for bobcats
in Ohio. Bobcats are classified as a furbearing animal in Ohio under
Ohio Revised Code Section 1531.01 and Ohio Administrative Code Rule
1501:31-1-02, while harvest is not currently permitted in Ohio, legal
harvest of bobcats occurs in 39 of the 47 states within their current
range. However, important population factors such as abundance and
density of the Ohio populations are currently unknown and bobcat density
varies widely across their range (Ferguson et al. 2009). Therefore,
research is needed to investigate density and abundance of bobcats in
the state to inform current and future management.

Genetic research conducted on bobcat samples from the early 2000s
indicated that bobcat recolonization in Ohio occurred sequentially with
two genetically distinct subpopulations in southern and eastern Ohio
(Anderson et al. 2015). The eastern population was founded from
individuals in West Virginia and was thought to be self-sustaining by
2012, whereas the southern population was dependent on continual
immigration from founder animals in Kentucky (Anderson et al. 2015).
Researchers also found differences in the average home-range size for
bobcats between these areas; bobcats in the southern Ohio area had
significantly larger home ranges and core areas then those in the
eastern area (Prange and Rose 2020). These regional differences in space
use could be a result of differences in habitat quality and degree of
population recovery, which would ultimately affect the density of
bobcats in the two areas. This has implications for management of the
bobcat population in Ohio, particularly if lethal harvest is to be
considered in this recovering population. For example, as part of
ongoing efforts to understand the long-term viability of bobcats in
Ohio, we determined that density was a critical parameter in predicting
future population trajectories via spatial population simulation models
(Dyck et al. In review). Thus, the applicability of these models to
inform management decisions is contingent on accurate bobcat density
estimates for different regions of Ohio. Although recent research shows
evidence of genetic admixture between the southern and eastern
populations (Heffern 2021), other sources of data (citizen sightings,
roadkill, camera trap) suggest that regional differences in density and
abundance likely still persist. Therefore, we predict that bobcat
density will be lower in southern Ohio compared to eastern Ohio.

Harvest data are used in many states to track population trends of
bobcats and inform management decisions (Roberts and Crimmins 2010).
However, given that these data are not available for Ohio's population,
we used non-invasive sampling to estimate density for bobcats in
southeast Ohio. Non-invasive sampling is a particularly useful tool for
monitoring cryptic and wide-ranging species such as carnivores (Kelly et
al. 2012; Davidson et al. 2014). We used DNA from scat as opposed to
motion-triggered cameras (another common non-invasive method used with
capture-recapture models to estimate animal density; Karanth and Nichols
1998; Royle et al. 2009) for our study because of the tendency for
bobcats in this region to have indistinct markings, thus leading to
unreliable individual animal identification (Morin et al. 2018). Studies
have found that non-invasive genetic surveys are an efficient
alternative to camera trap surveys for estimating abundance and density
of bobcats and other carnivores (Waits et al. 2001; Waits and Paetkau
2005; Ruell et al. 2009; Morin et al. 2018).

In this study, we implemented a multi-occasion scat sampling protocol at
three public land areas in southeast Ohio (1 in eastern and 2 in
southern Ohio) to estimate regional population abundance and density of
the returning bobcat population. We used capture histories generated
from the scat samples with spatially explicit capture-recapture (SECR)
analysis for density estimation (Efford 2022). The SECR framework
utilizes the spatial data associated with detectors to account for
animal movement and generate robust density estimates by avoiding biases
in calculating the effective sampling area (Efford 2004; Borchers and
Efford 2008; Royle and Young 2008).

\textbf{Methods}

\emph{Study area}

Our study took place on three major public land areas in Southeast Ohio
(Figure 1), Vinton Furnace State Experimental Forest (Vinton; 64 km2),
Zaleski State Forest (Zaleski; 116 km2), and AEP reclaimed strip-mining
lands (AEP; 239 km2). Vinton and Zaleski were in close proximity to each
other and are both included in the southern population from prior
research, therefore we aggregated the data from these two areas for
analysis (hereafter Vinton-Zaleski). Both study areas consist of
primarily forested land (AEP = 60.1\%, Vinton-Zaleski = 92.7\%)
interspersed with small patches of developed land (AEP = 3.8\%,
Vinton-Zaleski = 2.8\%; Figure 1). AEP is also characterized by moderate
areas of wetland habitat (11.1\%), water bodies (2.3\%), shrub/scrub
habitat (9.0\%), and open habitat, including grasslands (9.0\%),
pastures (10.7\%), and barren land (\textless{}1\%). Vinton-Zaleski has
proportionally fewer wetlands (1.4\%), water bodies (\textless{}1\%),
shrub/scrub (\textless{}1\%), and open habitat (\textless{}1\%).

\emph{Scat collection surveys}

Scat collection took place continuously from August 2018 to June 2019 in
Vinton, Zaleski, and AEP. Scats were collected daily along trail
transects at the field sites (AEP = 64.0 km and Vinton-Zaleski = 82.8
km); transects included dirt roads, gated access roads, and hiking
trails in each study area. Transects were re-surveyed two times (three
surveys in total) during the study. Weather prevented re-surveys for a
limited number of transects (\textless{}5\%) during the winter. Trained
field technicians searched for scats, collected samples, recorded GPS
locations, and tracked survey effort using Avenza Maps (Avenza Systems
Inc.). Scats were collected using a different set of latex gloves for
each sample to prevent transfer of DNA between samples and samples were
frozen at -10\selectlanguage{ngerman}°C the day of collection to preserve DNA.

\emph{DNA extraction and species identification}

DNA from collected scats were initially extracted using the QIAamp Fast
DNA Stool Mini Kits (Qiagen, Hilden, Germany) following the manufacturer
protocol. Subsequent re-extractions were performed using a modified
protocol to increase DNA quantity and included the following additional
steps: 1) duplicate samples were taken from each scat and combined
downstream, 2) samples were incubated overnight at 60°C prior to
homogenization, 3) 100ul of Buffer ATE was used for eluting DNA, 4)
samples were incubated at 60°C for 5 minutes prior to centrifuging for
elution, and 5) eluate was pipetted back onto the spin column, incubated
for 5 minutes, and centrifuged again. To prevent cross-contamination,
scats were extracted in a dedicated low-quality DNA processing lab and
gloves were changed between each sample. We designed species-specific
primers and probes for bobcat (mitochondrial genome subunit 5 gene;
GenBank: KP202285.1) and coyote (\emph{Canis latrans)} (mitochondrial
genome isolate 1 USA; GenBank: DQ480510.1) using Primer3 (Koressaar and
Remm 2007; Untergasser et al. 2012; Koressaar et al. 2018) and checked
for species specificity using NCBI's Primer-BLAST (Ye et al. 2012)
(Table 1). Quantitative Polymerase Chain Reactions (qPCR) for species
identification were performed in 15\selectlanguage{greek}µ\selectlanguage{english}l reactions containing 7.5\selectlanguage{greek}µ\selectlanguage{english}l
Quantitect Multiplex NO-Rox Master Mix (Qiagen), 0.75\selectlanguage{greek}µ\selectlanguage{english}l primer-probe mix
(primer concentration 8\selectlanguage{greek}µ\selectlanguage{english}M each, probe concentration 4\selectlanguage{greek}µ\selectlanguage{english}M), 0.3\selectlanguage{greek}µ\selectlanguage{english}l TaqMan
50X Exogenous IPC DNA (Applied Biosystems), 0.6\selectlanguage{greek}µ\selectlanguage{english}l TaqMan 10X Exogenous
Internal Positive Control (IPC) Block (Applied Biosystems), and 2.85\selectlanguage{greek}µ\selectlanguage{english}l
diH\textsubscript{2}O. Samples were run in triplicate on a 7500
Real-Time PCR System (Applied Biosystems) using standard dilutions made
from tissue DNA extracts for each species. Amplification curves were
visualized using the 7500 RT PCR System software. Negative controls were
included in each step downstream to identify cross-contamination. bobcat
positive samples were identified and DNA was used in subsequent steps.

\emph{DNA Amplification and Genotyping}

A suite of 8 dimorphic microsatellite primers originally designed for
domestic cats (Menotti-Raymond et al. 1999, 2005) and previously
amplified with bobcats (Morin et al. 2018) were optimized using bobcat
tissue DNA (Table 1). We used a multitube process to test the quality of
scat DNA and extraction success and eliminate samples of low quality.
All bobcat identified or potential samples were first amplified with a
primer multiplex containing 4 loci (\emph{FCA096, FCA275, FCA391, and
FCA126;} i.e. Multiplex 1). Samples that amplified at [?]1 locus were
re-extracted and samples that amplified at 2 loci (including
re-extractions) were re-amplified. Samples that amplified at 3-4 loci
then moved onto a subsequent multiplex (i.e., Multiplex 2) containing 4
primers (\emph{FCA090, FCA043, FCA124, and F124)} plus a sex
identification primer (\emph{AmelX/Y} ; Pilgrim et al. 2005). DNA was
amplified in 15\selectlanguage{greek}µ\selectlanguage{english}l polymerase chain reactions (PCR) containing 4\selectlanguage{greek}µ\selectlanguage{english}l DNA
plus 7.5\selectlanguage{greek}µ\selectlanguage{english}l 2X Multiplex Master Mix (Qiagen), 1.5\selectlanguage{greek}µ\selectlanguage{english}l Q Solution (Qiagen),
0.375\selectlanguage{greek}µ\selectlanguage{english}l Bovine Serum Albumin, primer mix at 0.2\selectlanguage{greek}µ\selectlanguage{english}M concentration of each
forward and reverse primer, and 0.425\selectlanguage{greek}µ\selectlanguage{english}l of diH\textsubscript{2}O.
Reactions were initiated at 95\selectlanguage{ngerman}°C for 15 minutes to activate hot-start
TAQ, then denatured at 94°C for 30 seconds, annealed at 55°C for 3
minutes, and extended at 72°C for 1 minute for 40 cycles; then finished
with a final extension at 60°C for 30 minutes. Amplified product was
plated with 9.5\selectlanguage{greek}µ\selectlanguage{english}l formamide and 0.5\selectlanguage{greek}µ\selectlanguage{english}l GeneScan 500 LIZ dye Size Standard
(Applied Biosystems) and genotyped on an ABI 3730xl at the Genomics
Shared Resource in The James Comprehensive Cancer Center at The Ohio
State University (NCI Cancer Center Support Grant P30CA016058).
Genotypes were scored using Geneious Prime software (version 2021.0,
Dotmatics, Boston MA). Samples were amplified 2-9 times at each locus
and included in the dataset used for individual analyses if they had at
least 2 consensus genotypes for homozygotes and at least 3 consensus
amplifications of each allele for heterozygotes at 5 or more loci.

\emph{Individual bobcat identification}

Individual bobcats were identified in program COLONY (version 2.0.6.7,
Jones and Wang 2010) using the clone method to identify and group
matching genotypes (Wang 2016). Parameters used included male and female
monogamy with inbreeding and with clone; diploid and dioecious options
selected; the Full-Likelihood method with long run time selected; no
updating of allele frequencies; sibship scaling; and no sibship prior.
Probability of identity (P\textsubscript{ID}) and probability of
identity for siblings (P\textsubscript{IDsib}) were calculated in
GenAlEx (version 6.503; Peakall and Smouse 2006, 2012) to assess the
power of the microsatellite marker set to identify individuals.

\emph{Spatially explicit capture-recapture}

We used a spatially explicit capture-recapture (SECR) framework to
estimate bobcat density (Efford and Fewster 2013). Because we performed
the sampling on pre-determined transects revisited three times during
the study area, we created ``detectors'' by splitting the study areas
into 1 km x 1 km grid cells. Only grid cells that overlapped the
transects were retained and we defined `detectors' as the centroid of
each grid cell. We then assigned all bobcat-identified scats collected
on the transect/s in a given cell the unique code of that cell (or
detector) (Royle et al. 2014). Bobcats can move large distances (several
km in a day) and have large home ranges averaging 15.83 to 39.70
km\textsuperscript{2} (Ferguson et al. 2009); the distance between the
center of each cell and locations of scats were therefore negligible
from a bobcat movement and space use perspective and assigning the scats
location to the cell centroid facilitated the development of capture
history data and data analysis.

The following modeling framework and workflow used package
\emph{secr}(Efford 2022) implemented in the program R (R Core Team
2022). We used ArcGIS (ESRI, Redlands CA) to create the habitat mask
used as an effective sampling area in our analysis. To model detection,
we calculated the sigma (\selectlanguage{greek}σ\selectlanguage{english}) model parameter using a root pooled variance
function as a measure of 2D dispersion of the centroids, pooled over
individuals (Efford 2022). We found that a buffer width of 5 \selectlanguage{ngerman}× \selectlanguage{greek}σ \selectlanguage{english}around
our detector array reduced the probability of capturing a bobcat outside
this buffer to zero and increasing buffer width beyond this value had no
discernable effect on the estimated density (Figure 2). This area is
thus typically used as the effective sampling area in spatial
capture-recapture models (Borchers and Efford 2008). The value of \selectlanguage{greek}σ \selectlanguage{english}was
1230 m. To investigate differences between the 2 study areas, AEP and
Vinton-Zaleski, we built a habitat mask by creating buffers around the
detectors equal to 5 \selectlanguage{ngerman}× \selectlanguage{greek}σ \selectlanguage{english}(6152 m) in ArcGIS; the resulting mask had two
different polygons, corresponding to the two study areas, and they had
an area of 543 km\textsuperscript{2} (AEP) and 580
km\textsuperscript{2}(Vinton-Zaleski).

We tested several detection functions and selected a `\emph{cumulative
lognormal} ' detection function to use in subsequent analyses, as this
function performed better than other detection functions based on Akaike
Information Criterion corrected for small sample size (AICc) (Akaike
1998) comparisons of SECR models fit with half-normal, compound
half-normal, cumulative gamma, and cumulative lognormal (Table 3). We
also compared several predictor variables for detection including length
of transect per grid cell (\emph{t\_length} ), and various habitat
variables (proportion of developed, forest, open, and wetland habitat)
against a constant detection (null) model. We found that the constant
detection model performed the best, but several other models were
\textless{}2 \selectlanguage{greek}Δ\selectlanguage{english}AICc from this model (Table 4). The model that included
detection as a function of the length of transect per grid cell failed
some variance calculations and thus was not included in model
comparison.

We fit a SECR state (observation) model using a spatial Poisson process
for animal activity centers (Borchers and Efford 2008) and included a
categorical predictor (study area: AEP or Vinton- Zaleski), as we
expected differences in density between the two areas based on
preliminary studies (Prange and Rose 2020; Popescu et al. 2021). We
compared this model to a constant density model (null) using AICc.
Lastly, because the data were collected within a single year (July 2018
to April 2019), it included a single birth pulse and each survey was
conducted over the course of several months, we did not investigate
potential differences in density between the three surveys. Instead, we
quantified the overall bobcat density and abundance during the study
period and differences between the two focal areas.

\textbf{Results}

\emph{Scat collection and genotyping}

We surveyed approximately 146.8 km of trails between the two study areas
(AEP = 64.0 km and Vinton-Zaleski = 82.8 km) for each survey event. We
collected a total of 813 scats during the three combined survey events
and were able to extract DNA from 789 scats for species identification.
Of those samples, we identified 37.4\% (295 scats) as bobcat for density
estimation. The remaining scats were identified as either coyote
(\emph{Canis latrans} ) 32.3\% (255 scats) or were of unknown species
30.3\% (239 scats). There were 25 samples identified as both coyote and
bobcat which were included in the first round of amplifications; none of
these samples amplified at any of the four MP1 loci and were removed and
classified as unknown species. Of the initial 295 bobcat samples
amplified at Multiplex 1, 164 reached the criteria to move onto
amplification with Multiplex 2. We achieved consensus genotypes at a
minimum of 5 loci for 102 samples (34.6\% genotype success rate) of
which 48 samples reached consensus at all 8 loci. Overall PCR
amplification success across all samples was typical for scat studies at
50.9\% and ranged from 27.2\%-75.9\% per locus (Table 2. The
probabilities that two individual genotypes (P\textsubscript{ID}) and
two sibling genotypes (P\textsubscript{IDsibs}) are identified as the
same individual for the 8-locus dataset was low at 8e-8 and 1.5e-3,
respectively, illustrating the high power of the locus set to
distinguish individuals.

We identified a total of 55 individual bobcats from 102 unique captures
(genotyped scats); the AEP study area had a higher number of individuals
(n = 33) compared to the Vinton-Zaleski study area (n = 22). Overall, we
identified more female bobcats than male bobcats (female = 28, male =
19, unknown = 8). We also identified more female bobcats in AEP (female
= 20, male = 8, unknown = 5), while Vinton-Zaleski had a more even
distribution of sexes (female = 8, male = 11, unknown = 3). Recapture
rates (those with multiple scats) were similar between sites (average of
1.97 \selectlanguage{ngerman}± 0.17 SE detections per individual in AEP and 2.31 ± 0.34 SE
detections per individual in Vinton-Zaleski), and sexes (average of 2.42
± 0.35 SE detection per individual for males and 2.04 ± 0.18 SE
detections per individual for females). The maximum number of detections
for any individual was 9 (male in Vinton-Zaleski), and 47 bobcats were
recaptured at least once. Of the 47 bobcats that were recaptured, the
average distance between successive recaptures was 1.68 km ± 0.25 SE.
The minimum distance between successive recaptures was 0 km (bobcats
recaptured within the same grid cell), and the maximum distance between
successive recaptures was 9.06 km (a male bobcat in AEP; Figure 3).

\emph{Bobcat density and abundance}

The top SECR model for estimating the density of bobcats in our study
areas included all individuals (n = 55) and estimated separate densities
per location with constant detection and a cumulative lognormal
detection function. This model performed significantly better than the
null model (\selectlanguage{greek}Δ\selectlanguage{english}AICc = 4.65). Based on this model, we estimated a density
of 17.9 \selectlanguage{ngerman}± 4.3 SE (13.6 -- 22.2) bobcats/100 km\textsuperscript{2} in AEP
and 11.3 ± 2.9 SE (8.4 - 14.2) bobcats/100 km\textsuperscript{2} in
Vinton-Zaleski.

\textbf{Discussion}

We provide the first assessment of bobcat density for the recovering
population in Ohio using eDNA from scats and SECR analysis. Bobcat
density differed between the AEP and Vinton-Zaleski study areas (17.9 ±
4.3 SE and 11.3 ± 2.9 SE bobcats/100 km\textsuperscript{2}respectively).
Our results support prior telemetry data which indicated that bobcats in
eastern Ohio (AEP area) had smaller home-ranges than bobcats in southern
Ohio (Vinton-Zaleski area) and thus could support a higher density of
individuals (Prange and Rose 2020) and camera trap studies that
indicated lower habitat occupancy by bobcats in the Vinton-Zaleski area
(Bencin 2018; Rich et al. 2018). However, the mechanism/s driving these
differences are not fully understood. Prange and Rose (2020)
hypothesized that differences in home-range sizes between the two areas
were a result of differences in food availability, habitat quality, and
body size as other studies have shown (Litvaitis et al. 1986; Anderson
1987; Knick 1990). While Rich et al. (2018) found landscape variables
had little effect on occupancy, but coyote presence had a strong
negative influence on bobcat occupancy. Bobcats and coyotes are
sympatric throughout most of North America and interference competition
between the two predators is rare (Dyck et al. 2022). However, bobcats
and coyotes have only recently begun to co-occur in the US Midwest
(Deems and Pursley 1978; Woolf and Hubert 1998). The concomitant
recolonization by bobcats (Reding et al. 2012) and range expansion by
coyotes (Hody and Kays 2018) represents a unique situation as species
are co-occurring that have shared evolutionary histories but have not
coexisted in recent time. While our study provides additional support
for regional differences in bobcat density and abundance, it does not
address the mechanisms driving these differences, and additional
research into this topic is needed and would provide useful insights for
bobcat management.

When comparing density estimates across the bobcat range from SECR
analyses, our estimates for AEP are at the higher end of those reported
elsewhere, while estimates from Vinton-Zaleski are within the average
(Clare et al. 2015; Thornton and Pekins 2015; Rounsville Jr 2018; Morin
et al. 2018; Jacques et al. 2019; Greenspan et al. 2020). However, when
comparing SECR density estimates for another recovering Midwest bobcat
population, the estimates from both study areas are much higher than
those reported for Illinois (1.4 individuals/100 km\textsuperscript{2};
Jacques et al. 2019). There are several factors that may explain
differences in bobcat density between our study and Illinois.

First, there are distinct differences in the type and distribution of
habitat between the two studies. Our study areas are characterized
primarily by forested habitat (60.1\% - 92.7\%) with minor areas of
developed land (2.8\% - 3.8\%; Figure 1), while the study site in
Illinois was dominated by agriculture (53.2\%) and pasture-hay (12.6\%)
with a lesser-degree constituting forest (27.3\%; Jacques et al. 2019).
Prior investigation into habitat suitability for the bobcat population
in Ohio indicated that bobcats select for forest habitat, natural
herbaceous vegetation habitat, and areas of low road density (Popescu et
al. 2021). Bobcat habitat suitability is also highest in southern and
southeast Ohio (Popescu et al. 2021), given our estimates are from high
suitability habitat it is likely that they represent the higher end of
density for the state.

Second, field methods between the two studies differed. Jacques et al.
(2019) used infrared-triggered cameras which are a common non-invasive
tool that can be used with capture-mark-recapture methods to estimate
density and abundance of bobcats and other felid species due to their
unique recognizable pelage patterns (Karanth and Nichols 1998; Silver et
al. 2004; Greenspan et al. 2020; Iosif et al. 2022). However, this
method requires quality images of both flanks of an animal and
sufficient variation in pelage/individual markings for observers to
accurately identify individuals. Jacques et al. (2019) deployed cameras
for 77 days and had 139 unique bobcat events but had to discard 18.7\%
due to low image quality and a small subset (6.2\%) of the remaining
images were classified as tentative identifications. Bobcat pelage
varies across their range (Young 1978; Croteau et al. 2012) and some
areas are known to have individuals with less distinct markings (Morin
et al. 2018).

Third, harvest of bobcats (hunting or trapping) is currently not
permitted in Ohio, while Illinois has had regulated hunting or trapping
season for bobcats since 2016 (Illinois DNR). The protected status of
bobcats in Ohio has played a role in their recovery and may act as a
mechanism for `mesopredator release' allowing population densities to
exceed those in states without protection. Legal harvest accounts for a
large portion of bobcat mortality in exploited populations (Rolley 1985;
Knick 1990; Fuller et al. 1995; Chamberlain et al. 1999; Blankenship et
al. 2006) and therefore if harvest mortality is additive with other
sources of mortality it is expected that population size would be higher
for unexploited populations such as Ohio.

\emph{Management and conservation implications}

Our results corroborate previous work suggesting uneven recovery of
bobcats in Ohio (Prange and Rose 2020), but that bobcat density is
relatively high in areas of suitable habitat. These findings highlight
the successful self-repatriation of a large carnivore after decades of
absence due to habitat recovery and improved land management practices.
Bobcats were extirpated from Ohio by 1850 coinciding with massive forest
clearing which reduced forest cover in the state from
\textasciitilde{}95\% to 10\% by the early 1900s. The development of the
first state forestry agency (now Division of Forestry) in 1885 and
continued efforts from this agency to purchase and protect Ohio forests
resulted in a 2.5-fold increase in forested land (\textasciitilde{}33\%)
by 2011 (Widmann et al. 2014). These efforts in combination with the
protection of bobcats under Ohio's state list of threatened and
endangered species were major factors contributing to the recovery of
this species.

Estimates of bobcat density are needed to validate and supplement
ongoing research into population viability for this recovering
carnivore. Outcomes of bobcat population simulation models were heavily
influenced by density (Dyck et al. In review) and our results can be
incorporated into these models to project future population dynamics
more accurately. These data can also be used by wildlife managers in
combination with prior habitat suitability analysis (Popescu et al.
2021) to inform delineations of harvest zones and regional-specific
quota limits to ensure sustainable management practices.

However, our results represent a snapshot in space and time during the
continual recovery process of bobcats in the Midwest and effective
bobcat management in Ohio requires continuous population monitoring,
including periodic estimates of density as the population continues to
expand (Popescu et al. 2021). Our study outlines a feasible, efficient,
and fully transparent method for estimating bobcat density that can be
repeated and applied to other areas and habitats. Results from this
study are also relevant at a regional level for other recovering
populations in the US Midwest and can be compared to indirect measures
of density such as maximum clique analysis (Jones et al. 2022) in
neighboring Indiana. Overall, the results from this study provides
critical information on density for recovering bobcats, outline a
feasible monitoring scheme to evaluate population density as recovery
continues, and can be used in combination with a variety of other
quantitative tools (e.g., population simulation models, habitat
suitability models) to improve management and conservation decisions for
recovering bobcats.

\textbf{Acknowledgments}

This project was funded by the Federal Aid in Wildlife Restoration
Program (W-134-P-20, Wildlife Management in Ohio), administered jointly
by the U.S. Fish and Wildlife Service and the Ohio Division of Wildlife.
Additional funding was provided by a Student Enhancement Award
administered by the Council on Research, Scholarship, \& Creative
Activity at Ohio University and awarded to M. A. Dyck. We thank Julia
Golias, Christine Hanson, Andrew Travers, Hannah Kopp, Ryan Brown, Megan
Sweeney, and Kaitlyn McKnight for conducting scat surveys. We also
acknowledge The Wilds for technical and material support. The authors
have no conflict of interest to declare.

\textbf{References}

Akaike H (1998) Information Theory and an Extension of the Maximum
Likelihood Principle. In: Parzen E, Tanabe K, Kitagawa G (eds) Selected
Papers of Hirotugu Akaike. Springer, New York, NY, pp 199--213

Anderson CS, Prange S, Gibbs HL (2015) Origin and genetic structure of a
recovering bobcat (Lynx rufus) population. Can J Zool 93:889--899.
https://doi.org/10.1139/cjz-2015-0038

Anderson EM (1987) A critical review and annotated bibliography of
literature on bobcat (Special Report 62). Colorado Division of Wildlife,
Colorado

Bencin HL (2018) Challenges of Conserving a Wide-ranging Carnivore in
Areas with Dense Road Networks. Master of Science, Ohio University

Blankenship TL, Haines AM, Tewes ME, Silvy NJ (2006) Comparing survival
and cause-specific mortality between resident and transient bobcats Lynx
rufus. Wildlife Biology 12:297--303.
https://doi.org/10.2981/0909-6396(2006)12{[}297:CSACMB{]}2.0.CO;2

Borchers DL, Efford MG (2008) Spatially Explicit Maximum Likelihood
Methods for Capture--Recapture Studies. Biometrics 64:377--385.
https://doi.org/10.1111/j.1541-0420.2007.00927.x

Ceballos G, Ehrlich PR, Dirzo R (2017) Biological annihilation via the
ongoing sixth mass extinction signaled by vertebrate population losses
and declines. PNAS 114:E6089--E6096.
https://doi.org/10.1073/pnas.1704949114

Chamberlain MJ, Leopold BD, Burger LW, et al (1999) Survival and
Cause-Specific Mortality of Adult Bobcats in Central Mississippi. The
Journal of Wildlife Management 63:613--620.
https://doi.org/10.2307/3802650

Clare JDJ, Anderson EM, MacFarland DM (2015) Predicting bobcat abundance
at a landscape scale and evaluating occupancy as a density index in
central Wisconsin. The Journal of Wildlife Management 79:469--480.
https://doi.org/10.1002/jwmg.844

Crooks KR (2002) Relative Sensitivities of Mammalian Carnivores to
Habitat Fragmentation. Conservation Biology 16:488--502

Croteau EK, Heist EJ, Nielsen CK, et al (2012) Microsatellites and
mitochondrial DNA reveal regional population structure in bobcats (Lynx
rufus) of North America. Conserv Genet 13:1637--1651.
https://doi.org/10.1007/s10592-012-0416-0

Davidson GA, Clark DA, Johnson BK, et al (2014) Estimating cougar
densities in northeast Oregon using conservation detection dogs: Cougar
Density Estimation. Jour Wild Mgmt 78:1104--1114.
https://doi.org/10.1002/jwmg.758

Deems EF, Pursley D (1978) North American furbearers, their management,
research, and harvest status in 1976.

Dirzo R, Young HS, Galetti M, et al (2014) Defaunation in the
Anthropocene. Science 345:401--406.
https://doi.org/10.1126/science.1251817

Dyck MA, Wyza E, Popescu VD (2022) When carnivores collide: a review of
studies exploring the competitive interactions between bobcats Lynx
rufus and coyotes Canis latrans. Mammal Review 52:52--66.
https://doi.org/10.1111/mam.12260

Efford M (2004) Density estimation in live-trapping studies. Oikos
106:598--610. https://doi.org/10.1111/j.0030-1299.2004.13043.x

Efford MG (2022) secr: Spatially explicit capture-recapture models.

Efford MG, Fewster RM (2013) Estimating population size by spatially
explicit capture--recapture. Oikos 122:918--928.
https://doi.org/10.1111/j.1600-0706.2012.20440.x

Ferguson AW, Currit NA, Weckerly FW (2009) Isometric scaling in
home-range size of male and female bobcats (Lynx rufus). Can J Zool
87:1052--1060. https://doi.org/10.1139/Z09-095

Fuller TK, Berendzen SL, Decker TA, Cardoza JE (1995) Survival and
Cause-Specific Mortality Rates of Adult Bobcats (Lynx rufus). American
Midland Naturalist 134:404. https://doi.org/10.2307/2426311

Greenspan E, Anile S, Nielsen CK (2020) Density of wild felids in
Sonora, Mexico: a comparison of spatially explicit capture-recapture
methods. European Journal of Wildlife Research 66:1--12.
https://doi.org/10.1007/s10344-020-01401-1

Heffern WJ (2021) Relatedness Assessment and Analysis of Road Mortality
Effects on Lynx rufus in Ohio. Ohio University

Hody JW, Kays R (2018) Mapping the expansion of coyotes (Canis latrans)
across North and Central America. ZooKeys 81--97.
https://doi.org/10.3897/zookeys.759.15149

Ibisch PL, Hoffmann MT, Kreft S, et al (2016) A global map of roadless
areas and their conservation status. Science 354:1423--1427.
https://doi.org/10.1126/science.aaf7166

Iosif R, Popescu VD, Ungureanu L, et al (2022) Eurasian lynx density and
habitat use in one of Europe's strongholds, the Romanian Carpathians.
Journal of Mammalogy gyab157. https://doi.org/10.1093/jmammal/gyab157

Jacques CN, Klaver RW, Swearingen TC, et al (2019) Estimating density
and detection of bobcats in fragmented midwestern landscapes using
spatial capture--recapture data from camera traps. Wildlife Society
Bulletin 43:256--264. https://doi.org/10.1002/wsb.968

Jones LR, Swihart RK, Gleich DF, et al (2022) Estimating statewide
carrying capacity of bobcats (Lynx rufus) using improved maximum clique
algorithms. Landsc Ecol. https://doi.org/10.1007/s10980-022-01460-6

Jones OR, Wang J (2010) COLONY: a program for parentage and sibship
inference from multilocus genotype data. Molecular Ecology Resources
10:551--555. https://doi.org/10.1111/j.1755-0998.2009.02787.x

Karanth KU, Nichols JD (1998) Estimation of Tiger Densities in India
Using Photographic Captures and Recaptures. Ecology 79:2852--2862.
https://doi.org/10.1890/0012-9658(1998)079{[}2852:EOTDII{]}2.0.CO;2

Kelly MJ, Betsch J, Wultsch C, et al (2012) Noninvasive sampling for
carnivores. Carnivore ecology and conservation: a handbook of techniques
47--69

Knick ST (1990) Ecology of Bobcats Relative to Exploitation and a Prey
Decline in Southeastern Idaho. Wildlife Monographs 3--42

Koressaar T, Lepamets M, Kaplinski L, et al (2018) Primer3\_masker:
integrating masking of template sequence with primer design software.
Bioinformatics 34:1937--1938.
https://doi.org/10.1093/bioinformatics/bty036

Koressaar T, Remm M (2007) Enhancements and modifications of primer
design program Primer3. Bioinformatics 23:1289--1291.
https://doi.org/10.1093/bioinformatics/btm091

Litvaitis JA, Sherburne JA, Bissonette JA (1986) Bobcat Habitat Use and
Home Range Size in Relation to Prey Density. The Journal of Wildlife
Management 50:110--117. https://doi.org/10.2307/3801498

Menotti-Raymond M, David VA, Lyons LA, et al (1999) A Genetic Linkage
Map of Microsatellites in the Domestic Cat (Felis catus). Genomics
57:9--23. https://doi.org/10.1006/geno.1999.5743

Menotti-Raymond MA, David VA, Wachter LL, et al (2005) An STR Forensic
Typing System for Genetic Individualization of Domestic Cat (\emph{Felis
catus} ) Samples. J Forensic Sci 50:1--10.
https://doi.org/10.1520/JFS2004317

Morin DJ, Waits LP, McNitt DC, Kelly MJ (2018) Efficient single-survey
estimation of carnivore density using fecal DNA and spatial
capture-recapture: a bobcat case study. Popul Ecol 60:197--209.
https://doi.org/10.1007/s10144-018-0606-9

ODNR (2018) Ohio Bobcat Management Plan. Columbus Ohio

Palomares F, Caro TM (1999) Interspecific Killing among Mammalian
Carnivores. The American Naturalist 153:492--508.
https://doi.org/10.1086/303189

Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in Excel.
Population genetic software for teaching and research. Molecular Ecology
Notes 6:288--295. https://doi.org/10.1111/j.1471-8286.2005.01155.x

Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel.
Population genetic software for teaching and research---an update.
Bioinformatics 28:2537--2539.
https://doi.org/10.1093/bioinformatics/bts460

Pilgrim KL, Mckelvey KS, Riddle AE, Schwartz MK (2005) Felid sex
identification based on noninvasive genetic samples. Molecular Ecology
Notes 5:60--61. https://doi.org/10.1111/j.1471-8286.2004.00831.x

Popescu VD, Kenyon M, Brown RK, et al (2021) Habitat connectivity and
resource selection in an expanding bobcat (Lynx rufus) population. PeerJ
9:. https://doi.org/10.7717/peerj.12460

Prange IS, Rose C (2020) Investigating uneven recovery of repatriated
bobcats (Lynx rufus) in a mined landscape: space use, habitat use and
condition in coal country. Wildl Res 47:77--88.
https://doi.org/10.1071/WR18204

Reding DM, Bronikowski AM, Johnson WE, Clark WR (2012) Pleistocene and
ecological effects on continental-scale genetic differentiation in the
bobcat (Lynx rufus). Molecular Ecology 21:3078--3093.
https://doi.org/10.1111/j.1365-294X.2012.05595.x

Rich M, Thompson C, Prange S, Popescu VD (2018) Relative Importance of
Habitat Characteristics and Interspecific Relations in Determining
Terrestrial Carnivore Occurrence. Frontiers in Ecology and Evolution 6:

Roberts NM, Crimmins SM (2010) Bobcat Population Status and Management
in North America: Evidence of Large-Scale Population Increase. Journal
of Fish and Wildlife Management 1:169--174.
https://doi.org/10.3996/122009-JFWM-026

Rolley RE (1985) Dynamics of a Harvested Bobcat Population in Oklahoma.
The Journal of Wildlife Management 49:283.
https://doi.org/10.2307/3801517

Rounsville Jr TF (2018) The Bobcats of West Virginia: How Many and Where
Are They? West Virginia University

Royle A, Chandler R, Sollman R, Gardner B (2014) Spatial
Capture-Recapture. Elsevier Inc.

Royle JA, Nichols JD, Karanth KU, Gopalaswamy AM (2009) A hierarchical
model for estimating density in camera\selectlanguage{english}-trap studies. Journal of applied
ecology 46:118--127

Royle JA, Young KV (2008) A Hierarchical Model for Spatial
Capture--Recapture Data. Ecology 89:2281--2289.
https://doi.org/10.1890/07-0601.1

Ruell EW, Riley SPD, Douglas MR, et al (2009) Estimating Bobcat
Population Sizes and Densities in a Fragmented Urban Landscape Using
Noninvasive Capture--Recapture Sampling. Journal of Mammalogy
90:129--135. https://doi.org/10.1644/07-MAMM-A-249.1

Silver SC, Ostro LET, Marsh LK, et al (2004) The use of camera traps for
estimating jaguar \emph{Panthera onca} abundance and density using
capture/recapture analysis. Oryx 38:148--154.
https://doi.org/10.1017/S0030605304000286

Thornton D, Pekins C (2015) Spatially explicit capture-recapture
analysis of bobcat (Lynx rufus) density: Implications for mesocarnivore
monitoring. Wildlife Research 42:. https://doi.org/10.1071/WR15092

Untergasser A, Cutcutache I, Koressaar T, et al (2012) Primer3---new
capabilities and interfaces. Nucleic acids research 40:e115--e115

Venter O, Sanderson EW, Magrach A, et al (2016) Sixteen years of change
in the global terrestrial human footprint and implications for
biodiversity conservation. Nature Communications; London 7:12558.
http://dx.doi.org.proxy.library.ohio.edu/10.1038/ncomms12558

Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of
identity among genotypes in natural populations: cautions and
guidelines. Molecular Ecology 10:249--256.
https://doi.org/10.1046/j.1365-294X.2001.01185.x

Waits LP, Paetkau D (2005) Noninvasive Genetic Sampling Tools for
Wildlife Biologists: A Review of Applications and Recommendations for
Accurate Data Collection. The Journal of Wildlife Management
69:1419--1433.
https://doi.org/10.2193/0022-541X(2005)69{[}1419:NGSTFW{]}2.0.CO;2

Wang J (2016) Individual identification from genetic marker data:
developments and accuracy comparisons of methods. Molecular Ecology
Resources 16:163--175. https://doi.org/10.1111/1755-0998.12452

Widmann RH, Randall CK, Butler BJ, et al (2014) Ohio's Forests 2011.
Resour. Bull NRS-90 Newtown Square, PA: US Department of Agriculture,
Forest Service, Northern Research Station

Woolf A, Hubert GF (1998) Status and Management of Bobcats in the United
States over Three Decades: 1970s-1990s. Wildlife Society Bulletin
(1973-2006) 26:287--293

Ye J, Coulouris G, Zaretskaya I, et al (2012) Primer-BLAST: A tool to
design target-specific primers for polymerase chain reaction. BMC
Bioinformatics 13:134--144. https://doi.org/10.1186/1471-2105-13-134

Young SP (1978) Bobcat of North America, its history, life habits,
economic status and control. University of Nebraska Press

Table 1: Microsatellite and species ID marker information for bobcat
(\emph{Lynx} \emph{rufus} ) and coyote (\emph{Canis latrans} ). The
upper area of the table contains information for 8 microsatellite loci
including number of samples (n), number of alleles (NA), observed
heterozygosity (Ho), expected heterozygosity (He), unbiased expected
heterozygosity (uHe), probability of identity (P\textsubscript{ID}), and
probability of identity siblings (P\textsubscript{IDsibs}). The lower
area of the table contains information for the species-specific primers
and probes for bobcat (LRUF) and coyote (CLAT).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
Locus & Sequence/Forward & Sequence/Reverse & Range (Bp) & n & Na & Ho &
He & uHe & P\textsubscript{ID} & P\textsubscript{IDsibs}\tabularnewline
\midrule
\endhead
FCA096 & CACGCCAAACTCTATGCTGA & CAATGTGCCGTCCAAGAAC & 165-227 & 43 & 7 &
0.419 & 0.642 & 0.650 & 1.9E-01 & 5.0E-01\tabularnewline
FCA275 & TTGGCTGCCCAGTTTTAGTT & ACGAAGGGGCAGGACTATCT & 106-146 & 54 & 5
& 0.333 & 0.375 & 0.379 & 7.6E-02 & 3.2E-01\tabularnewline
FCA391 & GCCTTCTAACTTCCTTGCAGA & TTTAGGTAGCCCATTTTCATCA & 160-276 & 49 &
7 & 0.490 & 0.757 & 0.765 & 6.4E-03 & 1.2E-01\tabularnewline
FCA126 & GCCCCTGATACCCTGAATG & CTATCCTTGCTGGCTGAAGG & 107-163 & 54 & 7 &
0.593 & 0.802 & 0.809 & 4.3E-04 & 4.6E-02\tabularnewline
FCA090 & ATCAAAAGTCTTGAAGAGCATGG & TGTTAGCTCATGTTCATGTGTCC & 100-112 &
52 & 6 & 0.596 & 0.733 & 0.740 & 4.5E-05 & 1.8E-02\tabularnewline
FCA043 & GAGCCACCCTAGCACATATACC & AGACGGGATTGCATGAAAAG & 112-124 & 52 &
6 & 0.481 & 0.578 & 0.584 & 1.0E-05 & 9.6E-03\tabularnewline
FCA124 & CCATTCCCTCCCTGTCTGTA & GCCTCAAGCCTCATTGCTAC & 125-145 & 47 & 9
& 0.702 & 0.775 & 0.783 & 8.0E-07 & 3.7E-03\tabularnewline
F124 & TGCTGGGTATGAAGCCTACT & ATTGCCTCAACTACCTAGGC & 150-190 & 44 & 8 &
0.659 & 0.777 & 0.786 & 8.0E-08 & 1.5E-03\tabularnewline
Amel X/Y & CGAGGTAATTTTTCTGTTTACT & GAAACTGAGTCAGAGAGGC & 195, 216- Male
216, 216- Female & 47 & 2 & 0.362 & 0.347 & 0.351 & x & x\tabularnewline
LRUF ID & AGTCACCGCTAACAACCTATTC & TGCATCTGTTCGGCCATATC & & & & & & &
&\tabularnewline
LRUF ID Probe & ACTGTTCATTGGCTGAGAAGGAGTAGG & x & & & & & & &
&\tabularnewline
CLAT ID & CAATCTCATGGGCTCCCTAA & GTCACAAGTGGAGGCCGTAT & & & & & & &
&\tabularnewline
CLAT ID Probe & TTGGCATGCATGATAGCATT & x & & & & & & & &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Locus & Multiplex & Samples ran (n) & Total Amplification Attempts &
Total Amplifications & Total Non-Amplifications & Amplification
Rate\tabularnewline
\midrule
\endhead
FCA096 & 1 & 318* & 1234 & 336 & 896 & 0.272\tabularnewline
FCA275 & 1 & 318* & 1241 & 799 & 440 & 0.644\tabularnewline
FCA126 & 1 & 318* & 1237 & 715 & 522 & 0.578\tabularnewline
FCA391 & 1 & 318* & 1241 & 478 & 761 & 0.385\tabularnewline
FCA090 & 2 & 164 & 862 & 654 & 208 & 0.759\tabularnewline
FCA043 & 2 & 164 & 862 & 520 & 342 & 0.603\tabularnewline
FCA124 & 2 & 164 & 857 & 436 & 421 & 0.509\tabularnewline
F124 & 2 & 164 & 861 & 427 & 434 & 0.496\tabularnewline
Amel & 2 & 164 & 864 & 352 & 512 & 0.407\tabularnewline
& & \textbf{Totals} & 9259 & 4717 & 4536 & \textbf{0.509}\tabularnewline
\bottomrule
\end{longtable}

Table 2: Polymerase Chain Reaction (PCR) amplification rates for bobcat
(\emph{Lynx rufus} ) scats at 8 loci. Scats were collected on public
lands in southeast Ohio between July 2018 and April 2019.

\emph{*Includes 25 samples that amplified as both L. rufus and C.
latrans}

Table 3: Model comparison of detection functions for spatially explicit
capture-recapture (SECR) models to determine the density of bobcats
(\emph{Lynx rufus} ) in southeast Ohio using eDNA from scats. For all
models, density (D), detection (g0), and movement (sigma) were constant.
Model fit was evaluated with Akaike Information Criterion correct for
small sample size (AICc).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model & Detection function & Parameters & Log likelihood & AICc & \selectlanguage{greek}Δ\selectlanguage{english}AICc
& Weight\tabularnewline
\midrule
\endhead
D \textasciitilde{} 1 g0 \textasciitilde{}1 sigma \textasciitilde{}1 &
Cumulative lognormal & 4 & -275.8 & 560.3 & 0.00 & 0.93\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}1 sigma \textasciitilde{}1 &
Cumulative gamma & 4 & -278.3 & 565.3 & 5.04 & 0.07\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}1 sigma \textasciitilde{}1 &
Halfnormal & 3 & -288.8 & 584.1 & 23.79 & 0.00\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}1 sigma \textasciitilde{}1 &
Compound halfnormal & 4 & -288.8 & 586.4 & 26.12 & 0.00\tabularnewline
\bottomrule
\end{longtable}

Table 4: Model comparison of detection variables (g0) for spatially
explicit capture-recapture (SECR) models to determine the density of
bobcats (\emph{Lynx rufus} ) in southeast Ohio using eDNA from scats.
Habitat detection variables represent the proportion of that habitat per
grid cell (1 km x 1 km). All models were fitted with the cumulative
lognormal detection function and both density (D) and movement (sigma)
were constant. Model fit was evaluated with Akaike Information Criterion
corrected for small sample size (AICc).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model & Detection variable & Parameters & Log likelihood & AICc & \selectlanguage{greek}Δ\selectlanguage{english}AICc
& Weight\tabularnewline
\midrule
\endhead
D \textasciitilde{} 1 g0 \textasciitilde{}1 sigma \textasciitilde{}1 &
Null & 4 & -277.9 & 564.6 & 0.00 & 0.26\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}forest sigma
\textasciitilde{}1 & Forest & 5 & -276.8 & 564.8 & 0.26 &
0.23\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}open sigma \textasciitilde{}1
& Open & 5 & -276.8 & 564.9 & 0.34 & 0.22\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}wetland sigma
\textasciitilde{}1 & Wetland & 5 & -276.9 & 565.1 & 0.53 &
0.21\tabularnewline
D \textasciitilde{} 1 g0 \textasciitilde{}developed sigma
\textasciitilde{}1 & Developed & 5 & -277.9 & 566.9 & 2.375 &
0.08\tabularnewline
\bottomrule
\end{longtable}

Figure 1: Study area for scat surveys of bobcats (\emph{Lynx rufus} ) in
southeast Ohio from August 2018 to June 2019. Solid black lines
represent transects which were surveyed three times each over the study
period. Yellow dots represent scat locations (n = 102) of genotyped
bobcat.

Figure 2: Effect of varying buffer width (meters) on estimated density
for spatially explicit capture-recapture (SECR) model of bobcats
(\emph{Lynx rufus} ) in southeast Ohio. Vertical lines represent various
buffer widths that were tested; 3691 m (orange), 4922 m (black), 6152 m
(red), and 7383 m (blue). We selected a buffer 6152 m (5 \selectlanguage{ngerman}× \selectlanguage{greek}σ\selectlanguage{english}).

Figure 3: Successive movements (meters) of individual bobcats
(\emph{Lynx rufus} ) with \textgreater{}1 capture identified by DNA from
scat and based on `detector' locations. `Detectors' were set as the
centroid of each grid cell that was surveyed, and spacing between
`detectors' was 1000 m.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1/Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig2-esa/fig2-esa}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig3-movement/fig3-movement}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

