u.s. department of agriculture u.s. government publication ... ·...
TRANSCRIPT
U.S. Department of Agriculture U.S. Government Publication Animal and Plant Health Inspection Service Wildlife Services
Estimating density of an elusive carnivore in urban areas: use of spatiallyexplicit capture-recapture models for city-dwelling bobcats
Julie K. Young1& Julie M. Golla2 & Derek Broman3
& Terry Blankenship4& Richard Heilbrun5
Published online: 8 February 2019# This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019
AbstractAn important first step in managing urban carnivores or the habitat in which they live to reduce risk of conflicts with humans is tounderstand their basic ecology and population dynamics. Traditional density estimators may be inappropriate in urban areasbecause of extensive areas of impermeable development but new techniques that include spatial structure could be useful withinlarge urban metropolitan areas. Yet to date, these techniques have largely remained untested. We evaluated whether spatiallyexplicit capture-recapture models (SECR) could provide a reliable density estimate of bobcats (Lynx rufus) in the Dallas Fort-Worth metroplex, Texas, USA.We obtained 1003 photographs of bobcats in an urbanized landscape from June–November 2014,using 41 double camera stations spaced approximately 1.05 km apart. We individually identified bobcats from their distinctpelage patterns and used SECR to predict density throughout the study area. The overall density was at least one bobcat per km2,which calculated to approximately 43 independent-aged bobcats across the entire camera grid, an estimate higher than docu-mented bobcat densities in both rural and peri-urban studies in Texas. Our study revealed a high density of bobcats in an urbanlandscape despite most assumptions that bobcats require large areas of habitat and are sensitive to fragmentation.
Keywords Camera trap . Carnivore ecology . Lynx rufus . Population estimate . SECRmodel
Introduction
Some of themost human-densely populated places in the worldare also home to thriving populations of mammalian carnivores(e.g., Athreya et al. 2013; Bhatia et al. 2013; Singh et al. 2010).These areas may become even more important to carnivores inthe future, serving as refuges from areas where carnivores arehunted or persecuted for killing livestock. Indeed, there is al-ready an increasing number of carnivores, especiallymesocarnivores, living in urban areas (Bateman and Fleming2012). Yet, as more carnivores co-occur with humans in urbanareas, the potential for conflict increases (e.g., Don Carlos et al.2009). Researchers are only beginning to understand carnivoreecology in these highly modified landscapes (Adams 2005;Magle et al. 2012). For example, leopards (Panthera pardus)in Mumbai, India, have recently been shown to benefit humanhealth and safety by suppressing the stray dog (Canisfamiliaris) populations (Braczkowski et al. 2018). However,leopards can also be a threat to human health and safety be-cause they may attack or kill humans. Thus, information onurban ecology of carnivores is imperative to identifying andmitigating human-carnivore conflict and assessing ecosystemservices of carnivores in urban areas.
* Julie K. [email protected]
Julie M. [email protected]
Derek [email protected]
Terry [email protected]
Richard [email protected]
1 USDA National Wildlife Research Center, Predator ResearchFacility and Department of Wildland Resources, Utah StateUniversity, 5230 Old Main Hill, Logan, UT 84322-5230, USA
2 Department of Wildland Resources, Utah State University,Logan, UT, USA
3 Texas Parks and Wildlife Department, Dallas, TX, USA4 Welder Wildlife Foundation, Sinton, TX, USA5 Texas Parks and Wildlife Department, San Antonio, TX, USA
Urban Ecosystems (2019) 22:507–512https://doi.org/10.1007/s11252-019-0834-6
One necessary component to understanding carnivore ecol-ogy in urban areas is to accurately estimate population sizeand density. Obtaining population size and density metrics onrare and elusive animals, likemost carnivores, can be extreme-ly challenging. Photographic capture-recapture methods havefacilitated efforts to estimate densities and have been usedsuccessfully for estimates of numerous felids, including bobcats(Lynx rufus; Thornton and Pekins 2015), tigers (Panthera tigris;Karanth and Nichols 1998), snow leopards (P. uncia; Alexanderet al. 2015), and jaguars (P. onca; Sollmann et al. 2011).Improvements to the capture-recapture based estimates for den-sity used in these earlier studies (Otis et al. 1978) have occurredwith the development of spatially explicit capture-recapture(SECR) methods (Borchers and Efford 2008; Royle et al.2009; Efford and Fewster 2013). In SECR modelling, animaldensity is estimated for marked animals while accounting forspatial locations of each capture. Incorporating spatial data canprovide better statistical performance than standard capture-recapture models, such as by increasing estimate precision(e.g., Blanc et al. 2013). The power of SECR relative to earliermark-recapture approaches has led to its growing popularity anduse in numerous methodological frameworks. SECR could beespecially useful in large urban areas due to the patchiness ofurban habitats and the ability of SECR models to calculatewithin-patch variation in densities (Alexander et al. 2015).
SECR has been proven effective to estimate densities ofseveral felids, including bobcats (Clare et al. 2015a;Thornton and Pekins 2015; Morin et al. 2018), tigers andleopards (Kalle et al. 2011), and West African lions (P. leo;Kane et al. 2015). Yet, these studies have typically occurred innatural landscapes and its utility in urban landscapes remainsunknown. SECR models have recently been used in a smallurban landscape in eastern Texas to estimate seasonal densi-ties of several mesocarnivore species, including coyotes(Canis latrans), bobcats, red foxes (Vulpes vulpes), and grayfoxes (Urocyon cinereoargenteus) (Lombardi et al. 2017). Toour knowledge, SECR has not been applied to carnivore pop-ulations in large urban environments.
Bobcats are elusive and sensitive to human disturbancesand were historically believed to be uncommon in areasdensely populated by humans (Crooks 2002). More recentlyresearch has shown bobcats exist in peri-urban environmentsnear or overlapping with areas of high human densities (Rileyet al. 2003; Alonso 2012) but no studies have examined bob-cat populations wholly surrounded by urban development.Bobcat populations can be estimated using individual identi-fication from spot patterns obtained via camera traps (e.g.,Thornton and Pekins 2015; Heilbrun et al. 2006). Thus, thegoal of our study was to determine population density of bob-cats in a large urban area using SECR models from cameratrap data. We predicted SECR models would be an effectivemethodology to obtain an estimate and that we would find ahigh density of bobcats within our study area.
Materials and methods
The Dallas-Fort Worth Metroplex (DFW), Texas, USA, is thefourth largest, third fastest growing, and 19th most densely pop-ulated metropolis in the United States (U.S. Census Bureau2014). It covers more than 24,000 km2 of rolling hills and largeflood plains that require extensive networks of storm drainagesand creeks to divert flood water into the Trinity River. Themetroplex lies within the cross timbers area of Texas, formerlycovered in habitat types such as oak trees (Quercus sp.) andbackland prairie (Gould 1975), and is now a mosaic of urbanstructures which includes retail stores, residential areas, cityparks, golf courses, and patches of undevelopable wetland, con-nected by a network of roads, highways, and interstates.
The study area was in the center of the DFW metroplex,bordered by state highways, an interstate, and the west fork ofthe Trinity River (Fig. 1). The area covered approximately78.1 km2 and includes developed parts of Fort Worth,Arlington, Hurst, Bedford, Euless, and Grand Prairie municipal-ities. The site was selected based on its high frequency of bobcatsightings by the public prior to our study period and completeimmersion within dense urban development. The area includedseveral types of urbanization including interstate rights of way,railroad tracks, industrial zones, residential neighborhoods, retailareas, developed open space, narrow linear patches of naturalhabitat (i.e., neighborhood greenbelts), and larger patches ofgreen space within the Trinity River riparian corridor.
To detect bobcats for SECR modeling, we used 41 double-camera stations. We first overlaid the study site with a 1 × 1km2 grid and identified the center point within each grid forthe camera station. For each station, we used a 300-m bufferaround the center point to ensure effective camera placementfor detecting resident bobcats while avoiding theft or tamper-ing by people, resulting in camera stations being an average of1.05 km apart. Grid spacing was based on the minimum ex-pected home-range size observed with preliminary data andother urban bobcat studies (Riley 2006; Alonso 2012). Weexcluded three camera-station grid sites due to a lack of accessto the buffer zone (Fig. 1).
For each station, we simultaneously placed two cameras onopposing sides of a road or trail, 2–5 m from the trail’s center,and aimed 30–45 cm above the trail (Heilbrun et al. 2006;Alonso 2012). No lure was used with camera sets to avoidany bias in detection (Heilbrun et al. 2003). Bushnell HDTrophy Camera model 119,576, Moultrie A-8 model MCG-12646, and one Reconyx PC900 HyperFire camera were usedacross the grid. The Bushnell and Reconyx cameras were pro-grammed to take bursts of three photographs per trigger at theirshortest trigger delay setting (≤ 1 s) to improve likelihood ofphotographing unique markings on photo-captured bobcats.The Moultrie cameras could only take one photograph percapture with a one-minute trigger delay; thus,Moultrie cameraswere only used when paired with either a Bushnell or Reconyx
508 Urban Ecosyst (2019) 22:507–512
(n = 37 stations) to limit detection bias based on camera type.Remaining stations (n = 4) had two Bushnell cameras. Stationswere active for 6–7 consecutive weeks across a single trappingperiod from 8 June - 15 November 2014.
Because we were conducting the study in an urban envi-ronment with heavy human traffic, we established a protocolfor two types of stolen-camera scenarios. If one camera wasstolen from a station, the remaining camera and a new camerawere re-set close by with additional camouflage on the sametrail until a total of six weeks of data could be collected from atleast one camera at that location. If both cameras were stolenfrom a station, an entirely new trail was selected within theinitial 300-m buffer for camera placement. The six-week in-terval started over at the new location.
We estimated population density using SECR package(Efford 2011) in R (R Core Team 2013). We first manuallyidentified individual bobcats using three physically unique fea-tures to match or differentiate individuals across photographcapture events following techniques described in Heilbrunet al. (2003). We used the same techniques to confidently iden-tify individual bobcats whether pictures existed from one ortwo cameras at a station (i.e., if one camera was stolen or didnot capture an image). Photographs of bobcats that could not beidentified were labeled as unknowns. One person identified allbobcats but a second person, with no prior knowledge of theoriginal assignments, was used to validate identification of in-dividuals. Discrepancies were discussed until consensus wasreached. We used the Photo Warehouse database software toorganize photographs of individual bobcats, both collared anduncollared (Ivan and Newkirk 2016). We then compiled thenumber of photographs of each individual at each camera
station across the entire survey grid to create individual capturehistories. A 20-min time interval was used to determine inde-pendent photograph capture events.
To include multiple captures of individuals over a singleoccasion, a capture history was developed (Efford 2011). InSECR, this is done using a count detector type with a capturefile. From our data we had a total of 265 capture events across23 occasions and all stations. Active dates for camera trapswere binned into weeks for occasion ID in the SECR capturehistory. To maximize capture inclusion, a camera was consid-ered active in a week if it was active for at least one night.Each bobcat capture event was assigned an occasion alongwith the station of detection.
SECR models use a habitat mask to represent habitataround the locations where detections occurred that is poten-tially the activity center for each individual of the populationbeing studied (Efford 2011). We created two different habitatmasks utilizing the 2011 National Land Cover Dataset(NLCD; Xian et al. 2011; Homer et al. 2015) to identify andremove areas of development from consideration for homerange centers. The two different masks we used (1) excludedall development and (2) excluded all high and medium densitydevelopment leaving low density development as potentialhome range centers; both masks excluded water (Xian et al.2011; Homer et al. 2015). The extent of the mask, called ahabitat buffer, for the camera grid was developed followingmethods detailed by Bashir et al. (2013; Fig. 1). A hazard half-normal detection function was selected, so that the models areparameterized in terms of the expected number of detections(i.e., cumulative hazard) and used with the Bcount^ detectortype as described in the SECR package (Efford 2011).
Fig. 1 a Location of DFWmetroplex in Texas, USA; (b)camera trap survey grid; (c, d)SECR habitat mask points,locations for potential home rangecenters as defined by theintersection of the effectivesampling distance of camera trapscalculated in SECR as ½MMDMand habitat suitability masks thatexcluded (c) only high andmedium development and (d) alldevelopment in the study sitewhere data was collected fromJune to November 2014. Scalebar is for panels (c) and (d)
Urban Ecosyst (2019) 22:507–512 509
Results
Six cameras were stolen from four camera stations, two whereboth cameras were stolen and two where only one camera wasstolen. No cameras were stolen a second time using our stolen-camera scenarios protocol. We had a few stations with tempo-rary camera failure (n = 3), but they were still considered ac-tive because one camera remained functional.
We obtained 279 bobcat capture events from 1003 photo-graphs of bobcats. From these photographs, we identified 42individuals. This included nine GPS-collared bobcats and twopreviously trapped but not-collared individuals. Two GPS-collared bobcats were not captured via camera traps althoughtheir GPS location data indicated they were present in the areaof the camera trap grid while the grid was active (Golla 2017).One bobcat caught on camera was not collared until aftercamera trapping ceased. Fourteen capture events were classi-fied as unknown.
Other carnivore species documented included coyote, rac-coon (Procyon lotor), gray fox, striped skunk (Mephitismephitis), Virginia opossum (Didelphis virginiana), domesticcat (Felis catus), and domestic dog (Canis familiaris). We alsophotographed other species, including fox squirrel (Sciurusniger), lagomorphs (Sylvilagus sp.), passerine and waterfowlspecies, nine-banded armadillo (Dasypus novemcinctus), bea-ver (Castor canadensis), domestic cows (Bos sp.), and feralpigs (Sus scrofa).
Using the ½ mean maximum distance moved method andthe habitat mask buffer of 727 m, analysis yielded an effectivecamera survey area of 32.8 km2 and 40.9 km2 for the twodifferent masks. The SECR model with a habitat mask thatexcluded all developed areas predicted 1.28 (0.95–1.72; 95%CI) bobcats/km2, whereas the model with the habitat maskthat excluded only high and medium development predicted1.03 (0.76–1.39; 95% CI) bobcats/km2 (Fig. 1). The estimatesyielded similar population estimates of 42.7 and 43.1 bobcats,respectively.
Discussion
In this study we report density of bobcats in a metropolitanarea using a rigorous design and analysis. We were capable ofsuccessfully applying SECR models using camera trap dataand found a dense population of bobcats living in the DFWstudy area. While this technique was highly successful in ourstudy system, alternatives to camera traps such as hair snaresfor DNA samples may be warranted in areas where issues,such as a lack of distinct spot patterns on their pelts (Strickeret al. 2012), hinder the ability to identify individuals. In thisstudy, where bobcats have distinct pelage, we were unable touse all photographs because we could not always distinguishpelage markings. Even so, we recommend camera trap data
when possible based on our results and the cost effectivenessof camera traps for detecting bobcats (Clare et al. 2015b).
We observed urban bobcat densities that were higher thanthose reported in peri-urban studies (Alonso 2012; Ruell et al.2012). Overall density was a little more than 1 bobcat/km2;this is more than the 0.64 bobcats/km2 reported for green spacenear a small urban community in east Texas (Lombardi et al.2017) and the 0.48 bobcats/km2 reported in rural south Texas(Heilbrun et al. 2006). The difference between our urbandensities and that reported in an urban area by Lombardiet al. (2017) may be related to the difference in timing or thedifferences in the degree of urbanization. Lombardi et al.(2017) only obtained density estimates for bobcats in fall,when dispersing individuals may cause a temporary changein density. Our study period began in summer and extendedinto fall, and is therefore more likely to represent the densitythroughout the year instead of specific to a given season.
Carnivore density is typically related to prey biomass(Carbone and Gittleman 2002) but is also influenced by avoid-ance of intraguild predators (Ritchie and Johnson 2009) andhumans (Valeix et al. 2012; Oriol-Cotterill et al. 2015).Mesocarnivores in and around urban areas have been shownto shift behavioral patterns in response to humans (Tigas et al.2002, Lowry et al. 2013) and home ranges and movementpatterns of bobcats are influenced by anthropogenic landscapefeatures such as roads (Poessel et al. 2014). Thus, the higherdensities observed here could be the result of one of manyecological factors that may differ between a built environmentand more natural areas where bobcats have been traditionallystudied. First, our study took place in the midst of a largermetropolitan area, where dispersal into and out of the sitemay be restricted. Indeed, wild cats are known to utilize nat-ural corridors for dispersal events, even within urban-wildlandinterfaces (e.g., Beier 1995), and such corridors may not existto a sufficient extent within the highly urban landscape of theDFW metroplex to facilitate dispersal. However, we do notthink this is the cause of the high density we observed withinthis particular study site because our study site was along theTrinity River corridor. Second, urban landscapes may offer anabundance of natural prey items that are easier to capture thanin natural settings. Studies have shown that anti-predator be-havior of prey items decrease in urban areas (e.g., McCleery2009). Finally, few bobcats are persecuted in this system andwemay have observed density estimates that would be similarto that in natural areas if no hunting occurs and most deathswere instead attributed to natural causes. For example, in cou-gars (Puma concolor) sport hunting is the primary source ofmortality in hunted populations but intraspecific strife is theprimary source of mortality in unhunted populations (e.g.,Wolfe et al. 2015). We had at least one GPS-collared bobcatshot by a human, another killed by a commuter train, andfound un-collared bobcats killed by vehicle traffic. Thus,human-caused mortality may be similar to natural areas even
510 Urban Ecosyst (2019) 22:507–512
though hunting and trapping may not exist in urban areas. It isclear that some species thrive in urban areas and behavioraladaptations may be important to their success (Lowry et al.2013). More information on DFW bobcats, collected over alonger time frame would help elucidate this issue.
We caution that our density estimates may be artificiallyinflated as potential bias in SECR estimates could exist dueto two of our field methods: (1) cameras were specificallyplaced on trails and in areas that would improve likelihoodof detecting bobcats in the area, thus potentially overestimatingdetection probabilities due to non-random placement withinthe 300-m buffer; and (2) only one camera survey sessionwas conducted, thus limiting the assumption that bobcat detec-tions were representative of the year-round population. Indeed,detection probability for bobcats was shown to be greater inurbanized habitat compared to wildland areas, potentially be-cause bobcats are funneled into the fewer corridors available inurban landscapes (Lewis et al. 2015). We note that populationestimates derived from this study using home range estimatesof GPS-collared bobcats suggest a much lower density of bob-cats (0.46 bobcats/km2; Golla 2017). Contrarily, our estimatesmay be low since some GPS-collared bobcats known to travelnear cameras were not detected (n = 2), indicating we did notachieve 100% detection with our camera grid despite themodels showing near perfect detection. Further, our densityestimates from GPS collars were based on discrete individualhome ranges but camera trap photos made it evident that GPS-collared bobcats spatially overlapped with uncollared bobcats.Additional research to compare SECR-based estimates to othertechniques would clarify accuracy.
The use of camera grids in urban landscapes to developSECR models is a novel application of a valuable tool in alandscape that poses unique challenges relative to more natu-ral landscapes. The mosaic of extreme and sudden differencesin habitat types poses great challenges to implementing a cam-era grid, especially when considering the high probability ofcamera theft or tampering due to increased human presence.The concerns of abrupt, impermeable boundaries impactingresults will continue to be an issue and efforts to mitigatepotential should be considered.
Our results provide the spatially explicit foundation to fur-ther investigate anthropogenic and environmental variablesimpacting densities of urban carnivores. Similar to most urbanlandscapes, the study area is prone to high rates of anthropo-genic disturbance (e.g. development, outdoor recreation),therefore repeating this effort could be one action to examinethe influence of those variables on bobcat densities. To trulymeasure and understand these impacts, we recommend esti-mating densities using SECR models before and after an areais developed. The rapid expansion of the DFW metroplex of-fers many opportunities to potentially implement such a studydesign. Understanding variations in densities of urban carni-vores over space and time could be key for their management.
Urbanization and loss of habitat is a leading threat to manylarge mammalian species, especially carnivores that often di-rectly compete with humans for resources (Crooks et al.2011). Obtaining information on urban bobcats will aid wild-life managers and municipal decision makers in continuingefforts to improve human-carnivore coexistence in urbanareas. Findings from our study revealed a high density ofbobcats in an urban landscape despite most assumptions thatbobcats require large areas of habitat and are sensitive to frag-mentation. A robust population of bobcats in the heart of adense metropolitan area, like DFW, provides optimistic possi-bilities for the potential of bobcats and other carnivores tothrive in urban landscapes with minimal conflict.
Acknowledgements We thank the many technicians and volunteers whohelped collect data and J. Draper for assistance with statistics and figures.We thank the editor and two anonymous reviewers for comments onearlier drafts. This study was funded by USDA-National WildlifeResearch Center, Utah State University, the Welder WildlifeFoundation, Texas Parks & Wildlife Department, and in part, byUSFWSWildlife Restoration Grant W139 T2-4 and by in-kind contribu-tions of Texas Parks &Wildlife Department volunteers. Any use of trade,firm, or product names is for descriptive purposes only and does notimply endorsement by the US government.
Compliance with ethical standards
Ethical approval This study was conducted in accordance with theUSDA’s National Wildlife Research Center’s Institutional Animal Careand Use Committee (IACUC) regulations (QA-2211).
Conflict of interest The authors have no conflict of interest.
References
Adams LW (2005) Urban wildlife ecology and conservation: a brief his-tory of the discipline. Urb Ecosyst 8:139–156
Alexander JS, Gopalaswamy AM, Shi K, Riordan P (2015) Face value:towards robust estimates of snow leopard densities. PLoS One 10:e0134815
Alonso RS (2012) The effects of urbanization and road development oncarnivores in Southern California. Dissertation, Colorado StateUniversity
Athreya V, Odden M, Linnell JDC, Krishnaswamy J, Karanth U (2013)Big cats in our backyards: persistence of large carnivores in a humandominated landscape in India. PLoS One 8:e57872
Bashir T, Bhattacharya T, Poudyal K, Sathyakumar S, Qureshi Q (2013)Estimating leopard cat Prionailurus bengalensis densities usingphotographic captures and recaptures. Wild Biol 19:462–472
Bateman PW, Fleming PA (2012) Big city life: carnivores in urban envi-ronments. J Zool 287:1–23
Beier P (1995) Dispersal of juvenile cougars in fragmented habitat. JWildl Manag 59:228–237
Bhatia S, Athreya V, Grenyer R, MacDonald DW (2013) Understandingthe role of representations of human-leopard conflict in Mumbaithrough media-content analysis. Cons Biol 27:588–594
Blanc L,Marboutin E, Gatti S, Gimenez O (2013) Abundance of rare andelusive species: empirical investigation of closed versus spatially
Urban Ecosyst (2019) 22:507–512 511
explicit capture–recapture models with lynx as a case study. J WildlManag 77:372–378
Borchers DL, Efford M (2008) Spatially explicit maximum likelihoodmethods for capture–recapture studies. Biometrics 64:377–385
Braczkowski AR, O'Bryan CJ, Stringer MJ, Watson JE, Possingham HP,Beyer HL (2018) Leopards provide public health benefits inMumbai, India. Frontiers Ecol Environ 16:176–182
Carbone C, Gittleman JL (2002) A common rule for the scaling of car-nivore density. Science 295(5563):2273–2276. https://doi.org/10.1126/science.1067994
Clare JD, Anderson EM, MacFarland DM (2015a) Predicting bobcatabundance at a landscape scale and evaluating occupancy as a den-sity index in Central Wisconsin. J Wild Manage 79:469–480
Clare JD, Anderson EM, MacFarland DM, Sloss BL (2015b) Comparingthe costs and detectability of bobcat using scat-detecting dog and re-mote camera surveys in CentralWisconsin.Wild Soc Bull 39:210–217
Crooks KR (2002) Relative sensitivities of mammalian carnivores tohabitat fragmentation. Cons Biol 16:488–502. https://doi.org/10.1046/j.1523-1739.2002.00386.x
Crooks KR, Burdett CL, Theobald DM, Rondinini C, Boitani L (2011)Global patterns of fragmentation and connectivity of mammalian car-nivore habitat. Phil Trans R Soc London B Biol Sci 366:2642–2651
Don Carlos AW, Bright AD, Teel TL, Vaske JJ (2009) Human–black bearconflict in urban areas: an integrated approach to management re-sponse. Human Dimens Wildl 14:174–184
Efford MG (2011) SECR-spatially explicit capture-recapture in R.University of Otago, Dunedin Google Scholar
Efford MG, Fewster RM (2013) Estimating population size by spatiallyexplicit capture–recapture. Oikos 122:918–928
Golla JM (2017) Urban bobcat (Lynx rufus) ecology in the Dallas-FortWorth, Texas Metroplex. Thesis, Utah State University
Gould FW (1975) The grasses of Texas. Texas A&M University, TexasAgricultural Experiment Station
Heilbrun RD, Silvy NJ, Tewes ME, Peterson MJ (2003) Usingautomatically-triggered cameras to individually identify bobcats.Wild Soc Bull 31:748–755
Heilbrun RD, Silvy NJ, Peterson MJ, Tewes ME (2006) Estimating bobcatabundance using automatically triggered cameras. Wild Soc Bull 34:69–73
Homer CG, Dewitz JA, Yang L, Jin S, Danielson P, Xian G, Coulston J,Herold ND, Wickham JD, Megown K (2015) Completion of the2011 national land cover database for the conterminous UnitedStates - representing a decade of land cover change information.Photogram Eng Remote Sens 81:345–354
Ivan JS, Newkirk ES (2016) CPWphoto warehouse: a custom database tofacilitate archiving, identifying, summarizing and managing photodata collected from camera traps. Methods Ecol Evol 7:499–504
Kalle R, Ramesh T, Qureshi Q, Sankar K (2011) Density of tiger andleopard in a tropical deciduous forest of Mudumalai Tiger Reserve,southern India, as estimated using photographic capture–recapturesampling. Acta Theriol 56:335–342. https://doi.org/10.1007/s13364-011-0038-9
Kane MD, Morin DJ, Kelly MJ (2015) Potential for camera-traps andspatial mark-resight models to improve monitoring of the criticallyendangered west African lion (Panthera leo). Biodivers Conserv 24:3527–3541
Karanth KU, Nichols JD (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures. Ecology 79:2852–2862
Lewis JS, Logan KA, Alldredge MW, Bailey LL, VandeWoude S, CrooksKR (2015) The effects of urbanization on population density, occupan-cy, and detection probability of wild felids. Ecol Appl 25:1880–1895
Lombardi JV, Comer CE, Scognamillo DG, ConwayWC (2017) Coyote,fox, and bobcat response to anthropogenic and natural landscapefeatures in a small urban area. Urban Ecosyst 20:1239–1248
Lowry H, Lill A, Wong BB (2013) Behavioural responses of wildlife tourban environments. Biol Rev 88(3):537–549
Magle SB, Hunt VM, Vernon M, Crooks KR (2012) Urban wildliferesearch: past, present, and future. Biol Conserv 155:23–32
McCleery RA (2009) Changes in fox squirrel anti-predator behaviorsacross the urban–rural gradient. Landsc Ecol 24:483–493
Morin DJ,Waits LP, McNitt DC, KellyMJ (2018) Efficient single-surveyestimation of carnivore density using fecal DNA and spatial capture-recapture: a bobcat case study. Pop Ecol 60:197–209
Oriol-Cotterill A, Valeix M, Frank LG, Riginos C, Macdonald DW(2015) Landscapes of coexistence for terrestrial carnivores: the eco-logical consequences of being downgraded from ultimate to penul-timate predator by humans. Oikos 124:1263–1273
Otis D, BurnhamK,White G, Anderson D (1978) Statistical inference fromcapture data on closed animal populations. Wild Monog 62:3–135
Poessel SA, Burdett CL, Boydston EE, Lyren LM, Alonso RS, FisherRN, Crooks KR (2014) Roads influence movement and homeranges of a fragmentation-sensitive carnivore, the bobcat, in an ur-ban landscape. Biol Conserv 180:224–232
R Core Team (2013) R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna,Austria. URL http://www.R-project.org/
Riley SP (2006) Spatial ecology of bobcats and gray foxes in urban andrural zones of a national park. J Wild Manage 70:1425–1435
Riley SPD, Sauvajot RM, Fuller TK, York EC, Kamradt DA, Bromley C,WayneRK (2003) Effects of urbanization and habitat fragmentation onbobcats and coyotes in southern California. Conserv Biol 17:566–576
Ritchie EG, Johnson CN (2009) Predator interactions, mesopredator re-lease and biodiversity conservation. Ecol Lett 12:982–998. https://doi.org/10.1111/j.1461-0248.2009.01347.x
Royle JA, Karanth KU, Gopalaswamy AM, Kumar NS (2009) Bayesianinference in camera trapping studies for a class of spatial capture–recapture models. Ecology 90:3233–3244. https://doi.org/10.1890/08-1481.1
Ruell AEW, Riley SPD, Douglas MR, Antolin MF, Pollinger JR, TraceyJA, Lyren LM, Boydsten EE, Fisher RN, Crooks KR (2012) Urbanhabitat fragmentation and genetic population structure of bobcats incoastal southern California. Amer Mid Nat 168:265–280. https://doi.org/10.1674/0003-0031-168.2.265
Singh P, Gopalaswamy AM, Karanth KU (2010) Factors influencing den-sities of striped hyenas (Hyaena hyaena) in arid regions of India. JMamm 91:1152–1159. https://doi.org/10.1644/09-MAMM-A-wide
Sollmann R, Furtado MM, Gardner B et al (2011) Improving densityestimates for elusive carnivores: accounting for sex-specific detec-tion and movements using spatial capture–recapture models for jag-uars in Central Brazil. Biol Conserv 144:1017–1024. https://doi.org/10.1016/j.biocon.2010.12.011
Stricker HK, Belant JL, Beyer DE Jr et al (2012) Use of modified snaresto estimate bobcat abundance. Wildl Soc Bull 36:257–263
Thornton DH, Pekins CE (2015) Spatially explicit capture–recaptureanalysis of bobcat (Lynx rufus) density: implications formesocarnivore monitoring. Wild Res 42:394–404
Tigas LA, Van Vuren DH, Sauvajot RM (2002) Behavioral responses ofbobcats and coyotes to habitat fragmentation and corridors in anurban environment. Biol Conserv 108:299–306
US Census Bureau (2014) Current estimates data. Washington, D.C: USCensus Bureau. Available at http://www.census.gov/popest/data/national/totals/2014/index.html
Valeix M, Hemson G, Loveridge AJ, Mills G, Macdonald DW (2012)Behavioural adjustments of a large carnivore to access secondaryprey in a human-dominated landscape. J Appl Ecol 49:73–81
Wolfe ML, Koons DN, Stoner DC, Terletzky P, Gese EM, Choate DM,Aubry LM (2015) Is anthropogenic cougar mortality compensatedby changes in natural mortality in Utah? Insight from long-termstudies. Biol Conserv 182:187–196
Xian G, Homer C, Dewitz J, Fry J, Hossain N, Wickham J (2011) Thechange of impervious surface area between 2001 and 2006 in theconterminousUnited States. PhotogramEngRemote Sens 77:758–762
512 Urban Ecosyst (2019) 22:507–512