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Use of Probability of Detection When Conducting Analyses of Surveys ofMesopredators: a Case Study from the Ozark Highlands of MissouriAuthor(s): Michael V. Cove, Liisa M. Niva, and Victoria L. JacksonSource: The Southwestern Naturalist, 57(3):257-261. 2012.Published By: Southwestern Association of NaturalistsDOI: http://dx.doi.org/10.1894/0038-4909-57.3.257URL: http://www.bioone.org/doi/full/10.1894/0038-4909-57.3.257

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THE SOUTHWESTERN NATURALIST 57(3): 257–261 SEPTEMBER 2012

USE OF PROBABILITY OF DETECTION WHEN CONDUCTINGANALYSES OF SURVEYS OF MESOPREDATORS: A CASE STUDY FROM

THE OZARK HIGHLANDS OF MISSOURI

MICHAEL V. COVE, LIISA M. NIVA, AND VICTORIA L. JACKSON*

Department of Biology and Earth Science, University of Central Missouri, Warrensburg, MO 64093*Correspondent: vjackson@ucmo.edu

ABSTRACT—We surveyed 14 communities of mesopredators in the Ozark Highlands of southern Missouri toexamine the effect of landscape and surveying efforts on probability of detection of raccoons (Procyon lotor)and Virginia opossums (Didelphis virginiana). Virginia opossums had a higher probability of detection thanraccoons. Mean size of forested patches had a negative effect on probability of detection, suggesting that thehypothesis that abundance of mesopredators increases in small patches of forest is an artifact of sampling. Wesuggest that it is important for researchers to include probability of detection when analyzing data fromsurveys of mesopredators.

RESUMEN—Muestreamos 14 comunidades de depredadores medianos en las Ozark Highlands del sur deMissouri para examinar el efecto de factores de paisaje y esfuerzos de muestreo en la probabilidad dedeteccion de mapaches (Procyon lotor) y tlacuaches (Didelphis virginiana). Los tlacuaches tuvieron unaprobabilidad de deteccion mayor que los mapaches. El tamano medio de parches de bosque tuvo un efectonegativo en la probabilidad de deteccion, sugiriendo que la hipotesis de que la abundancia de losdepredadores medianos suba en parches pequenos del bosque es artefacto de muestreo. Sugerimos que esimportante que los investigadores incluyan la probabilidad de deteccion cuando analicen datos de muestreosde depredadores medianos.

Management of mesopredators has become importantbecause, in the absence of large carnivores, these smallerpredators can become abundant and threaten migratorybirds and small mammals (Crooks and Soule, 1999;Sinclair et al., 2005). Three common mesopredators,Virginia opossums (Didelphis virginiana), raccoons (Procy-on lotor), and striped skunks (Mephitis mephitis), occursympatrically throughout most of North America. Similarrequirements for resources by these generalists haveallowed them to become ubiquitous members of mostnatural and human-altered forested communities(Schwartz and Schwartz, 2001).

While studies of larger predators often aim to estimatetrue abundance (Kays et al., 2008), studies that examineecology of mesopredators often use indices of abundancefor analysis: rate of capture (unique captures/100 trap-nights; Disney et al., 2008) and rates of visitation to scentstations or relative abundance (Crooks and Soule, 1999;Dijack and Thompson, 2000; Sinclair et al., 2005). Inseveral recent studies, researchers attempted to modelrelative abundance of raccoons, Virginia opossums, andstriped skunks as functions of landscape and localhabitats (Crooks and Soule, 1999; Dijack and Thompson,2000; Sinclair et al., 2005; Disney et al., 2008) to predictrisk of predation for forest-nesting birds. In their study in

Missouri, Dijack and Thompson (2000) suggested thatabundance of raccoons was related positively to latitude,density of streams, and mean size of patches onagricultural lands, whereas abundance of Virginia opos-sums was related positively to mean distance betweenpatches of forest, latitude, density of streams, and relatednegatively to contagion. They determined that abun-dance of striped skunks did not relate to any character-istic of landscape examined. Other studies (Crooks andSoule, 1999; Sinclair et al., 2005; Disney et al., 2008) havesuggested that abundance of mesopredators increases assize of forested patches decreases.

Although variable detection may be accounted for tosome degree in indices of abundance, the assumption isthat probability of detection is constant and unaffected byhabitat, surveying effort, or surveying method. Disney etal. (2008) attempted to compare indices of abundancebetween rates of visitation at scent stations and rates ofcapture for raccoons and Virginia opossums in afragmented-forested landscape, but detected no associa-tion between the two methods. This suggests that indicesof abundance, in general, are not appropriate forpredicting true relationships of abundance or habitat.As an alternative to abundance or indices of abundance,MacKenzie et al. (2005, 2006) suggested using the state-

variable occupancy (W) when trying to elucidate relation-ships of habitat or distribution of species. This approachuses maximum-likelihood models to estimate occupancyby incorporating the additional parameter of probabilityof detection (p), which also can vary as a result ofcovariates of the model.

We suspect that surveying effort and covariates ofhabitat (i.e., heterogeneity of habitat and size of forestedpatch) will have effects on probability of detection andwill bias indices of abundance if these are not accountedfor prior to analysis. Our study aimed to examine howthese variables affect probability of detection for threemesopredators in the Ozark Highlands of southernMissouri. In particular we attempted to model covariatesthat were revealed to be significant predictors ofabundance from two studies in the central United States(Dijack and Thompson, 2000; Disney et al., 2008), as wellas other predictors that we believed to be biologicallysignificant.

MATERIALS AND METHODS—Our study area included 10 countiesin the Ozark Highlands of southern Missouri from RandolphCounty as the northern limit and the Arkansas border as thesouthern limit. Surveys were conducted on public landsmanaged by the Missouri Department of Conservation,Missouri Department of Natural Resources, the United StatesForest Service, and one private farm. Although trappingfurbearers was legal and could be conducted on the lands inour study area, no reported trapping occurred at our sites priorto or during our surveys. We selected 14 sites with 5 sites at thenorthern edge of the Ozark Highlands and 9 sites in thesouthern portion of the region. The northern and southern siteswere separated by >100 km and all sites were >4 km apart toensure independence and allow analysis of landscape similar toDijack and Thompson (2000). The use of 14 sites was similar to astudy by O’Connell et al. (2006) in which they used 13 sites toestimate parameters of occupancy and detection for mediumand large mammals.

Second-growth Quercus-Carya (oak-hickory) and mixed-hard-wood forests dominate the Ozark Highlands, which areinterspersed with woodlands, savannas, prairies, and agriculturallands (Nigh and Schroeder, 2002). Our trapping sites primarilyconsisted of oak and mixed-hardwood forests; however, grass-lands, croplands, and wildlife food plots also were common.Little was noted regarding composition of the understory, butmost sites were characterized by nonnative shrubs, cool-seasongrasses, and forbs. Average temperature during our surveys was8.48C (range, 3.1–18.78C).

During October 2008–April 2009, we set medium (106) andlarge (108) Tomahawk live traps (Tomahawk Live Trap Co.,Tomahawk, Wisconsin) >100 m apart along established animaltrails and natural funnels. Number of traps deployed at each sitewas 8–22 and was dependent on size of the area and availabilityof traps. This led to differences in trapping effort: 75 – 8 SEtrapnights/site (range, 28–137), but our analysis accounted forthis variation. Because we believed that inclement weather andlow temperatures would affect detection negatively, we did nottrap during precipitation events or when temperature was <08C.We baited traps with mackerel or sardines and marshmallowsand checked traps during 0700–1200 h. Initially, traps were set

and run for 2–4 consecutive nights. After the last evening, trapswere locked open, revisited after a 3–5-day rest period, and runagain for an additional 2–4 evenings. Missing data fromincomplete surveys at some sites (<8 full days) was accommo-dated by the occupancy-modeling approach (MacKenzie et al.,2006).

Once animals were captured, they were anesthetized with anintramuscular injection of Ketamine hydrochloride-Aceproma-zine (10 mg/kg and 1mg/kg, respectively). Standard measure-ments and gender were recorded for all animals that werehandled. In some instances, animals were released withoutbeing handled or escaped before being handled. Each animalalso received an individually numbered Monel ear tag (NationalBand and Tag Co., Newport, Kentucky). Following recovery,animals were released at the site of capture. All procedures werein accordance with guidelines of the American Society ofMammalogists (Gannon et al., 2007) and the University ofCentral Missouri Institutional Animal Care and Use Committee(permit 10-3209).

Using ArcGIS 9.3.1 (Environmental Systems ResearchInstitute, Redland, California), we overlaid all trappinglocations onto a digitized land-use-land-cover map. Followingthe analysis of landscape by Dijack and Thompson (2000), wecreated a 2-km-radius buffer at each site using a central pointamong the traps to measure covariates of landscape. Wemeasured cumulative lengths of all roads and streams andused the Patch Analyst extension in ArcGIS 9.3.1 (R. Rempel,http://flash.lakeheadu.ca/~rrempel/patch/index.html) tomeasure total forested edge, mean size of forested patch, andtotal number of patches within each buffer. To test thehypothesis that latitude affects detection of mesopredators,we classified sites as northern and southern groups. We alsoincluded surveying effort per site (trapnights) as a covariate tobe used in modeling. We standardized all continuous covariatesto z-scores for analysis, but no other transformation wasperformed (Long et al., 2011). We did not include seasonalvariation or covariates of temperature because our surveys wereconducted over the course of one season. Additionally, becausewe did not trap in poor weather or low temperatures, webelieve that any seasonal or temperature variation had little tono effect on our estimates.

Lack of recaptures of individual mesopredators precludedestimation of true abundance for our study sites. Prior toanalysis, we decided to group Virginia opossums and raccoonsinto one model due to their similar, generalist, ecologicalrequirements, but maintained an additional covariate of speciesto model species-specific effects. We kept models of strippedskunks separate because we anticipated different effects ofcovariates based on previous studies (Dijack and Thompson,2000; Disney et al., 2008). We then compiled all trappingrecords to create a binary history of detection (detected = 1,not detected = 0) for all three mesopredators. We developed13 a priori models (Table 1) based on biologically plausibleexplanations of occurrence and detection and results ofprevious research (Dijack and Thompson, 2000; Disney et al.,2008). We included a null model with no effect of covariatesand a global model that contained all eight possible covariatesto ensure that there was no covariate interaction that lead tononconvergence in the saturated model. For analysis, we used asingle-season analysis in program PRESENCE 2.4 (J. E. Hines,http://www.mbr-pwrc.usgs.gov/software/presence.html). Be-

258 vol. 57, no. 3The Southwestern Naturalist

cause our naıve estimates of occupancy were high, we believedraccoons and Virginia opossums occurred at all sites and wefixed the parameter for occupancy to 1.0 and only evaluatedprobabilities of detection as affected by covariates of surveyingand habitat. This is similar to the first step of the two-stepprocess employed by Yates and Muzika (2006) and Long et al.(2011) to first determine how variables affect probability ofdetection and then use those covariates as a constant set whenderiving estimates of occupancy.

The best approximating models were selected based on theAkaike Information Criterion corrected for small samples(AICc) and Akaike weights (wi). We selected the 95% confidenceset (summed wi = 0.95) of the supported models and removedthe remaining models that were not contained in theconfidence set to redistribute the Akaike weight among thetop models. We then conducted model averaging (Burnhamand Anderson, 2002) using spreadsheet software designed by B.Mitchell (www.uvm.edu/%7Ebmitchel/software.html) to esti-mate species-specific probabilities of detection and effects ofcovariates across multiple models.

RESULTS—We detected six species of mammals duringthe 1,052 trapnights from 73 sampling occasions. Themost commonly detected mesopredator was the Virginia

opossum (n = 83), followed by the raccoon (n = 47).Striped skunks were captured only on four occasions andwere excluded from analyses. Other incidental capturesincluded the eastern woodrat (Neotoma floridana, n = 3),eastern fox squirrel (Sciurus niger, n = 2), and easterncottontail (Sylvilagus floridanus, n = 1)

Of the 13 a priori models, only seven were containedin the 95% confidence set. A difference in probability ofdetection between species was supported by five of theseven models comprising the 95% confidence set (Table1). Raccoons had a lower probability of detection thanVirginia opossums, 0.46 – 0.04 SE and 0.64 – 0.05 SE,respectively. Mean size of forested patch appeared as anegative covariate in three of the top models (Tables 1and 2), which agreed with the direction of our a priorihypothesis. Latitude, trapping effort, and total numberof patches were all contained in the 95% confidence setof the model, but had little effect on parameters ofdetection with confidence intervals that strongly over-lapped zero (Tables 1 and 2). Directions of effect ofcovariates, however, all agreed with our a priorihypotheses. Streams, roads, and total forested edge were

TABLE 1—Descriptions and expected direction of a priori models (except the global model with all covariates) for detection ofmesopredators from surveys conducted in the Ozark Highlands of southern Missouri, October 2008–April 2009.

Hypothesis Model Structure of model Expected result

No effects of habitat or survey ondetection

p(.) b0 —

Increasing mean size of forested patchwill affect detection negatively

p(mean size of forested patch) b0 + b1(mean size of forestedpatch)

b1 < 0

Increasing trapping effort at sites willaffect detection positively

p(trapping effort) b0 + b1(trapping effort) b1 > 0

Increasing total number of patches willaffect detection positively

p(total number of patches) b0 + b1(total number ofpatches)

b1 > 0

Species-specific detection and positiveinfluence from latitude

p(species + latitude) b0 + b1(species) +b2(latitude)

b1 < 0, b2 > 0

Species-specific detection and negativeinfluence from roads

p(species + road) b0 + b1(species) + b2(road) b1 < 0, b2 < 0

Species-specific detection and positiveinfluence from streams

p(species + stream) b0 + b1(species) +b2(stream)

b1 < 0, b2 > 0

Species-specific detection and positiveinfluence from increasing total edge offorest

p(species + total edge of forest) b0 + b1(species) + b2(totaledge of forest)

b1 < 0, b2 > 0

Species-specific detection and positiveinfluence from increasing total numberof patches

p(species + total number ofpatches)

b0 + b1(species) + b2(totalnumber of patches)

b1 < 0, b2 > 0

Species-specific detection and positiveinfluence from increasing trappingeffort

p(species + trapping effort) b0 + b1(species) +b2(trapping effort)

b1 < 0, b2 > 0

Species-specific detection and negativeinfluence from increasing size offorested patch

p(species + mean size of forestedpatch)

b0 + b1(species) + b2(meansize of forested patch)

b1 < 0, b2 < 0

Species-specific detection, negativeinfluence from mean size of forestedpatch, positive influence from latitude,and positive influence from increasingtrapping effort

p(species + mean size of forestedpatch + latitude + trappingeffort)

b0 + b1(species) + b2(meansize of forested patch)+ b3(latitude) +b4(trapping effort)

b1 < 0, b2 < 0,b3 > 0, b4 > 0

September 2012 Cove et al.—Analysis of mesopredator surveys 259

not contained in the 95% confidence set and appearedto have no effect on probability of detection.

DISCUSSION—We determined that several covariatesinfluenced ability to detect mesopredators in oursurveys. All of these covariates have been suggested asdrivers of abundance of mesopredators from otherstudies (Crooks and Soule, 1999; Dijack andThompson, 2000; Sinclair et al., 2005; Disney et al.,2008), but these studies did not consider probability ofdetection in their analyses and we caution use of indicesof abundance for drawing inferences about landscape.We suggest that modeling occupancy and the statevariable of occupancy is a more appropriate approachto modeling relationships of habitat of mesopredators.This approach also is advantageous because multi-seasonand co-occurrence models of species have beendeveloped and can be used to make strong inferencesabout factors that affect extinction and colonization ofsites and dynamics of community interactions(MacKenzie et al., 2006).

Our surveys used varying numbers of traps per site dueto availability of traps (raccoons often damaged themedium-sized traps and rendered them unusable) andconstraints of forested patches. Disney et al. (2008) alsoused varying numbers of traps (2–14) at different forestedsites, leading to 80–560 trapnights/site. The occupancy-modeling approach allowed us to test how trapping effortaffected our ability to detect presence of mesopredators.Although we believe that future surveying efforts shouldbe standardized as much as possible, our results suggestthat surveying effort (i.e., varying number of traps persite) explained only limited variation in our ability todetect mesopredators. We acknowledge that largerdifferences in trapnights per site may have morepronounced effects on probability of detection, but thiscan be modeled as a covariate of site to improveinferences in future surveys.

We were unable to estimate occurrence or probabilityof detection of striped skunks due to few detections.Neither the study by Dijack and Thompson (2000) northat of Disney et al. (2008) was able to collect sufficient

data for striped skunks to make inferences about theirabundance. Our trapping results also demonstrated thatstriped skunks are detected infrequently and futurestudies should aim to improve surveying techniques forthis species.

Dijack and Thompson (2000) suggested that abun-dance of raccoons and Virginia opossums in Missouri wasinfluenced by latitude. They noted that agricultural landsin the northern portion of the state were dominated bycroplands, whereas agricultural lands in the southernportion of the state were used more often for hay andpasture. Agricultural crops do provide an additionalsource of food for mesopredators, but there is acorrelation between increasing agricultural lands anddecreasing size of forested patches (Dijack and Thomp-son, 2000). Because we were unable to estimate trueabundance from our surveys, we were unable to examinea numerical response for mesopredators to thesealterations of landscape. However, interspecific andintraspecific interference competition often drive dy-namics of communities and populations of carnivores(Palomares and Caro, 1999) and, in this instance,dynamics of mesopredators. Smaller patches of forestsurrounded by agricultural land should not exhibit atrue increase in abundance of mesopredators due toincreased interference competition and limited resourc-es associated with the isolated patch.

TABLE 3—Model-averaged estimates of coefficients for covar-iates, unconditional standard errors, and 95% CIs in models ofdetection comprising the 95% confidence set from surveys ofmesopredators conducted in the Ozark Highlands of southernMissouri.

Covariate b estimate SE Lower CI Upper CI

Intercept 0.194 0.206 –0.210 0.598Species –0.575 0.198 –0.963 –0.187Mean size of

forested patch –0.548 0.164 –0.869 –0.227Latitude 0.431 0.266 –0.090 0.952Trapping effort 0.001 0.001 –0.001 0.003Total number of

patches 0 0 0 0

TABLE 2—Statistics for models of probability of detection derived from surveys of mesopredators conducted in the Ozark Highlandsof southern Missouri. Included are models from the 95 % confidence set.

Model AICc DAICc Akaike weightNumber ofparameters -2 log-likelihood

W(.),p(species + mean size of forested patch) 120.48 0.00 0.351 4 110.74W(.),p(mean size of forested patch) 120.98 0.49 0.274 3 113.98W(.),p(species + latitude) 121.18 0.70 0.247 4 111.44W(.),p(species + mean size of forested patch +

latitude + trapping effort)123.88 3.40 0.064 6 107.88

W(.),p(species + trapping effort) 125.96 5.48 0.023 4 116.22W(.),p(species + total number of patches) 126.12 5.64 0.021 4 116.38W(.),p(trapping effort) 126.27 5.79 0.019 3 119.27

260 vol. 57, no. 3The Southwestern Naturalist

Our results that probabilities of detection are higher atnorthern sites and that probability of detection is relatednegatively to size of forested patch corresponds withsuggestions of Dijack and Thompson (2000) and Disneyet al. (2008) because higher abundance might influenceprobability of detection. However, the exact relationshipbetween abundance and probability of detection has notbeen examined. Furthermore, the use of scent stations inthese studies (Dijack and Thompson, 2000; Disney et al.,2008) cannot distinguish between individual animals andthe high rates of detection for this method are also a poorindicator of true abundance because a single individualcan visit all stations at a single site in one evening (Disneyet al., 2008). Our results suggest that activities ofmesopredators are more restricted in small patches offorest within fragmented landscapes causing an increasein the probability of detection, which creates an artifactthat abundance also is high.

We do not believe that any study has producedsignificant evidence that true abundance of mesopreda-tors increases in smaller patches of forest. Disney et al.(2008) suggested that rates of visitation at scent stationswere measures of activity by animals and that activity ofraccoons and Virginia opossums were concentrated insmaller patches of forest, leading to increased risk ofpredation on forest-nesting birds. We believe that thishypothesis more accurately reflects the increased risk ofpredation on nests than does the suggestion thatabundance of mesopredators is higher in smaller patchesof forest.

Although our study was modest, we believe that therelationship between probability of detection and pa-rameters in our analysis is evident and, with largersamples in future studies, the exact relationship anddriving forces can be explored more robustly. Relativeabundance and rates of capture are inaccurate indices ofabundance, unless modeled as functions of sampling andcovariates of habitat. We suggest that occupancy model-ing is a more appropriate approach, but abundance ofmesopredators also can be estimated directly by markingindividuals and using capture-recapture analyses, or byusing the repeated-count models of Royle and Nichols(2003). This is evidence that future surveys will benefitfrom inclusion of a parameter for probability ofdetection when objectives include estimating occupancyof sites (patches) or relationships of habitats forcommon mesopredators and exploring risk of predationon nests.

This research was funded by the Missouri Department ofConservation. We thank personnel of the Missouri Departmentof Conservation, Department of Natural Resources, and theUnited States Forest Service, as well as the Corson family, forallowing us access to their land for our surveys. We thank D.Fantz and anonymous reviewers for their help.

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Submitted 5 April 2011. Accepted 10 May 2012.Associate Editor was Jennifer K. Frey.

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