Humans transform natural ecosystems worldwide into towns and cities, replacing natural habitat with human-built surfaces. This loss of habitat and increase in human activity make suburban areas difficult for some species to survive in, raising concerns that developed areas become ecologically unbalanced as they lose biodiversity. However, the preservation of urban green space and lack of hunting could also open the door for some species to thrive in the midst of large human populations. Indeed, some animals, mammals in particular, have grown more tolerant of humans and appear to have adapted to suburban landscapes around the world. Some species that have been exclusively living in the wilderness, such as a small carnivore called the fisher, are even moving back into cities.

Research into how mammals are coping with the urbanization of their habitats has produced conflicting results. Studies that explore a variety of cities and habitats would help to clear up this confusion.

Parsons et al. worked with citizen scientist volunteers to survey the mammals at 1,427 sites across Washington DC and Raleigh, North Carolina. The volunteers set up motion-triggered cameras in these sites, which covered a full range of urban and wild habitats, including back yards and large nature preserves.

The cameras detected similar or higher numbers of mammal species in suburban sites compared to wild areas. Indeed, most species appear to use suburban areas at least as much as wild land. Urban green space is especially important; it is used by less urban-adapted species like coyotes to navigate areas that are densely populated by humans.

The results presented by Parsons et al. suggest that many mammals have indeed adapted to the suburban environment over the last few decades, resulting in more balanced urban ecosystems. More testing in other cities will help to determine how general this pattern of adaptation is, and provide us with knowledge that could help us to conserve many different species. However, some species were still most abundant in wild areas, emphasizing the need to also conserve wildlands and to minimize our impact on natural ecosystems.

Global loss of biodiversity leads to disruption of ecosystem services around the world, ultimately threatening human well-being (Cardinale et al., 2012). Vertebrate species loss is typically considered to be worst in the most developed landscapes, where urbanization serves as an intense and long-term disturbance that permanently alters habitat and truncates food webs (Lombardi et al., 2017; McKinney, 2006). However, for some species, urbanization can offer abundant nutrient-rich food that is less ephemeral compared to wild areas (Bateman and Fleming, 2012; Wang et al., 2017). Whether this food is enough to counteract the negative effects of disturbance (i.e. higher road mortality, fragmentation) depends on a species’ ability to adapt to the stressors of urban living (Witte et al., 1982). Mammal species, especially those with large home ranges, are arguably most at risk from development, leading some to suggest that developed areas have a dearth of predators, and that prey species could benefit by using humans as a shield (Crooks, 2002; Ordeñana et al., 2010). Previous studies have shown cities to be depauperate of bird life, supporting the traditional view that development and biodiversity cannot coexist (Keast, 1995; Strohbach et al., 2014).

However, recent evidence has shown that some mammal species previously thought mal-adapted to urban landscapes (i.e. mountain lion [Puma concolor], fisher [Martes pennanti]) are thriving in them (Bateman and Fleming, 2012; LaPoint et al., 2013), suggesting an evolutionary trend that could be important for conservation in the Anthropocene. Existing research on mammal communities across urbanization gradients has focused on single cities, yielding conflicting results, perhaps due to variation in city structure and characteristics (Lombardi et al., 2017; Saito and Koike, 2013). Given the rapid expansion of urban areas worldwide, and the recent case studies of urban adaptations by wildlife (LaPoint et al., 2013; Riley et al., 2014; Wang et al., 2017), more large-scale studies are needed to evaluate the response of wildlife communities to urban development if we are to understand urban ecology, conservation, and evolution in the Anthropocene.

Here, we present the results of a large-scale mammal survey of two urban-wild gradients. Our objectives were to determine how diversity, richness, detection rate, and occupancy of the mammal community change as a function of human disturbance. We hypothesized that the availability of supplemental food at higher levels of development would positively affect mammalian populations and outweigh the negative effects of disturbance, except for the most sensitive species. Specifically, we predicted that mammalian relative abundance would increase with developmental level but that species richness and diversity would decrease. Furthermore, we predicted that occupancy of the most sensitive species (i.e. large and medium carnivores) would be highest in wild areas both in our study area and around the world.

Working with citizen scientist volunteers, we obtained 53,273 detections of 19 mammal species at 1427 sites along an urban-wild gradient in Washington, DC and Raleigh, NC, USA, sampling both private and public lands. In DC, we detected 17 mammal species with mean naïve occupancy of 0.19 (min = 0, max = 0.93) and mean detection rate of 0.09 detections/day (min = 0, max = 1.05). In Raleigh, we detected 17 mammal species with mean naïve occupancy of 0.14 (min = 0, max = 0.79) and mean detection rate of 0.08 detections/day (min = 0, max = 0.09).

We found no significant decline of species diversity or richness from suburban to wild gradient levels (Figure 2—figure supplement 1, Figure 1). However, Shannon diversity was significantly lower at the urban level in DC, possibly due to low sampling (Figure 2, Supplementary file 1). Diversity in yards was significantly higher or not statistically different from large and small forest fragments in both cities (Figure 2—figure supplements 2,3). Most (92.3%) of the 13 mammal species detected >20 times occupied all levels of development below the urban level. Two of the largest predators, coyotes and bobcats, were absent from the highest development level (urban) but were detected at all other levels in both cities. Black bears (Ursus americanus), which are actively discouraged from colonizing central North Carolina (North Carolina Wildlife Resources Commission, 2011), were not detected in Raleigh and were detected in DC at all levels of the gradient except suburban and urban, though were predominantly in the wild level. These results indicate that the extant mammal guild exploits all levels of the urban-wild gradient and that no species are entirely relegated to the wild gradient level. However, some species appear less adapted to habitation in human-dominated areas, spending most of their time at the wild levels of the gradient (i.e. bobcat, bear; Figure 1). We recognize that the current community represents species that survived the initial arrival of high-density human settlement. In particular, two large predators (wolves (Canis lupus) and cougars [Puma concolor]) were extirpated from our study area a century ago. However, even cougars and wolves have recently shown surprising adaptability in the face of development at other sites (Bateman and Fleming, 2012; Wang et al., 2017) suggesting that, given enough time and protection from persecution, many of the most ‘wild’ of species may adapt to human development.

Figure 1

Download asset Open asset

Figure 2 with 3 supplements see all

Download asset Open asset

Predators are thought to be the most at risk from urbanization (Crooks, 2002), therefore, we evaluated predictors for occupancy (MacKenzie et al., 2002) and detection rate (Kays et al., 2017) for four carnivores: coyote, gray fox, red fox, and bobcat. Both of our models fit well, with Bayesian p-values between 0.1 and 0.9 (Supplementary file 3). Suburban and urban occupancy probabilities were not statistically different from wild for any of the species (Figure 3—figure supplement 1) and we noted a decreasing trend in occupancy from urban to wild (Figure 3). We compared the occupancy estimates from our study to those reported for carnivores in protected areas around the world (Rich et al., 2017) and found no significant difference (Figure 3), suggesting that the ecological function of predators in this urban system is not substantially reduced from the current wild state, excepting the historical extirpation of the two largest native predators from the region.

Figure 3 with 1 supplement see all

Download asset Open asset

Our occupancy and detection rate models yielded similar results (Supplementary file 5–7) demonstrating that green space is important to carnivore species that are less-adapted to human-altered landscapes. These models show a greater association of carnivores with green space when housing density is high (e.g. coyote and gray fox, Supplementary file 6, 7), consistent with other studies finding urban green space important in maintaining biodiversity in cities (Gallo et al., 2017; Lombardi et al., 2017; Matthies et al., 2017). It is likely that shyer species are not avoiding regions of high human density, but require patches of forest to navigate residential areas that are freely used by more commensal species, such as red foxes (Tigas et al., 2002), which we frequently detected in yards. Indeed, we found a gradient of responses in carnivore use of human-dominated environments, from red fox which are the most urban adapted (i.e. negatively associated with local large forest fragments and the only species to have a positive association with yards) to bobcats which appear to be the most human-averse (i.e. rarely detected in the suburban level of the gradient) (Figure 1; Figure 3—figure supplement 1).

Contrary to expectations, we found no evidence for a negative impact of suburban and exurban development on extant native mammal diversity, richness, and occupancy and detection rate of carnivores. In fact, all metrics were significantly greater than, or equal to, wild areas. We suspect that developed areas offer good food resources for wildlife through direct and indirect feeding (i.e. bird feeders supplementing prey, pets), accidental feeding (i.e. garbage), and ornamental plantings (for herbivores), but testing this hypothesis will require additional diet studies in urban landscapes (Contesse et al., 2004). Furthermore, the structure of mature suburbia (i.e. older, established neighborhoods with large trees, wooded riparian areas, small parks) contributes to a more diverse and varied landscape than wild areas with more homogenous forest cover, which is potentially beneficial for many generalist species. Developed areas where hunting is limited or prohibited also offer a safe haven for game species, presuming they can navigate the road networks (Collins and Kays, 2011) and avoid direct human conflict.

Our discovery of a wild suburbia suggests high levels of adaptation by mammals to developed landscapes over the last few decades, including predators and prey. The resilience of these species gives hope for wildlife in the Anthropocene, but the generality of this pattern needs to be tested in other cities to understand how habitat type, development patterns, apex predators, and hunting regulations influence urban mammal communities, as there are examples of far more drastic impacts of urbanization on other taxa and in other places around the globe (Keast, 1995; McKinney, 2008). Indeed, in Tokyo, Japan, the relative abundance of mammals declined with urbanization (Saito and Koike, 2013) and avian communities in Quebec, Canada and Rennes, France showed a similar decline in richness (Clergeau et al., 1998; Saito and Koike, 2013). This suggests that city structure, size and human density may influence mammalian distribution along urban-wild gradients with large, sprawling New World cities showing different patterns than the smaller more concentrated cities of the Old World. Although our study provides a less dire picture of urban ecosystem function than previously thought, we do not suggest abandoning mitigation of urbanization’s negative impacts, or conservation of completely wild areas. Factors such as urban green space, connectivity and availability of completely wild areas give species the time and space to adapt to changing habitats and climates. Further understanding of how urban wildlife navigates human-dominated areas and factors that contribute to the adaptation of species to the Anthropocene will be critical to maintaining diversity in a rapidly urbanizing world.

Raw detections data have been deposited in Data Dryad, doi:10.5061/dryad.11rf64v. The software used for initial species identification is available via eMammal.org. To download and use the software, users must first create an account on eMammal and become associated with an existing project. This can be done by using the 'Join' button on the project's homepage, or by emailing the contact person, also listed on the project homepage. Usually the user will also have to pass an online or in person training, depending on the project requirements, and they will then become approved to download the software.

1. 1. Arielle Waldstein Parsons
   2. Tavis Forrester
   3. Megan C Baker-Whatton
   4. William McShea
   5. Christopher T Rota
   6. Stephanie G Schuttler
   7. Joshua J Millspaugh
   8. Roland Kays

   (2018) Wild suburbia: mammal communities are larger and more diverse in moderately developed areas

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.11rf64v

1. 1. Bateman PW
   2. Fleming PA

   (2012) Big City life: carnivores in urban environments

   Journal of Zoology 287:1–23.

   https://doi.org/10.1111/j.1469-7998.2011.00887.x

   • Google Scholar
2. 1. Bekoff M

   (1977) Canis latrans

   Mammalian Species 79:1–9.

   https://doi.org/10.2307/3503817

   • Google Scholar
3. 1. Burton AC
   2. Neilson E
   3. Moreira D
   4. Ladle A
   5. Steenweg R
   6. Fisher JT
   7. Bayne E
   8. Boutin S

   (2015) REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes

   Journal of Applied Ecology 52:675–685.

   https://doi.org/10.1111/1365-2664.12432

   • Google Scholar
4. 1. Cardinale BJ
   2. Duffy JE
   3. Gonzalez A
   4. Hooper DU
   5. Perrings C
   6. Venail P
   7. Narwani A
   8. Mace GM
   9. Tilman D
   10. Wardle DA
   11. Kinzig AP
   12. Daily GC
   13. Loreau M
   14. Grace JB
   15. Larigauderie A
   16. Srivastava DS
   17. Naeem S

   (2012) Biodiversity loss and its impact on humanity

   Nature 486:59–67.

   https://doi.org/10.1038/nature11148

   • PubMed
   • Google Scholar
5. 1. Chao A
   2. Gotelli NJ
   3. Hsieh TC
   4. Sander EL
   5. Ma KH
   6. Colwell RK
   7. Ellison AM

   (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies

   Ecological Monographs 84:45–67.

   https://doi.org/10.1890/13-0133.1

   • Google Scholar
6. 1. Clergeau P
   2. Savard J-PL
   3. Mennechez G
   4. Falardeau G

   (1998) Bird abundance and diversity along an Urban-Rural gradient: a comparative study between two cities on different continents

   The Condor 100:413–425.

   https://doi.org/10.2307/1369707

   • Google Scholar
7. 1. Collins C
   2. Kays R

   (2011) Causes of mortality in north american populations of large and medium-sized mammals

   Animal Conservation 14:474–483.

   https://doi.org/10.1111/j.1469-1795.2011.00458.x

   • Google Scholar
8. 1. Contesse P
   2. Hegglin D
   3. Gloor S
   4. Bontadina F
   5. Deplazes P

   (2004) The diet of urban foxes (Vulpes vulpes) and the availability of anthropogenic food in the city of Zurich, Switzerland

   Mammalian Biology - Zeitschrift Für Säugetierkunde 69:81–95.

   https://doi.org/10.1078/1616-5047-00123

   • Google Scholar
9. 1. Crooks KR

   (2002) Relative sensitivities of mammalian carnivores to habitat fragmentation

   Conservation Biology 16:488–502.

   https://doi.org/10.1046/j.1523-1739.2002.00386.x

   • Google Scholar
10. 1. Dodge S
    2. Bohrer G
    3. Weinzierl R
    4. Davidson SC
    5. Kays R
    6. Douglas D
    7. Cruz S
    8. Han J
    9. Brandes D
    10. Wikelski M

    (2013) The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

    Movement Ecology 1:3.

    https://doi.org/10.1186/2051-3933-1-3

    • PubMed
    • Google Scholar
11. 1. Dorazio RM
    2. Royle JA
    3. Söderström B
    4. Glimskär A

    (2006) Estimating species richness and accumulation by modeling species occurrence and detectability

    Ecology 87:842–854.

    https://doi.org/10.1890/0012-9658(2006)87[842:ESRAAB]2.0.CO;2

    • PubMed
    • Google Scholar
12. 1. Dorazio RM
    2. Royle JA

    (2005) Estimating size and composition of biological communities by modeling the occurrence of species

    Journal of the American Statistical Association 100:389–398.

    https://doi.org/10.1198/016214505000000015

    • Google Scholar
13. 1. Fritzell EK
    2. Haroldson KJ

    (1982) Urocyon cinereoargenteus

    Mammalian Species 189:1–8.

    https://doi.org/10.2307/3503957

    • Google Scholar
14. 1. Gallo T
    2. Fidino M
    3. Lehrer EW
    4. Magle SB

    (2017) Mammal diversity and metacommunity dynamics in urban green spaces: implications for urban wildlife conservation

    Ecological Applications 27:2330–2341.

    https://doi.org/10.1002/eap.1611

    • PubMed
    • Google Scholar
15. 1. Gelfand AE
    2. Schmidt AM
    3. Wu S
    4. Silander JA
    5. Latimer A
    6. Rebelo AG

    (2005) Modelling species diversity through species level hierarchical modelling

    Journal of the Royal Statistical Society: Series C 54:1–20.

    https://doi.org/10.1111/j.1467-9876.2005.00466.x

    • Google Scholar
16. Book

    1. Gelman A
    2. Carlin JB
    3. Stern HS
    4. Dunson DB
    5. Vehtari A
    6. Rubin DB

    (2014)

    Bayesian Data Analysis (Third edition)

    CRC Press.

    • Google Scholar
17. 1. Hammer RB
    2. Stewart SI
    3. Winkler RL
    4. Radeloff VC
    5. Voss PR

    (2004) Characterizing dynamic spatial and temporal residential density patterns from 1940–1990 across the North Central United States

    Landscape and Urban Planning 69:183–199.

    https://doi.org/10.1016/j.landurbplan.2003.08.011

    • Google Scholar
18. 1. Hooten MB
    2. Hobbs NT

    (2015) A guide to Bayesian model selection for ecologists

    Ecological Monographs 85:3–28.

    https://doi.org/10.1890/14-0661.1

    • Google Scholar
19. Software

    1. Hsieh TC
    2. Ma KH
    3. Chao A

    (2016)

    iNEXT: iNterpolation and EXTrapolation for Species Diversity , version (Version 2.0.12)

    iNEXT: iNterpolation and EXTrapolation for Species Diversity .
20. 1. Kays R
    2. Parsons AW
    3. Baker MC
    4. Kalies EL
    5. Forrester T
    6. Costello R
    7. Rota CT
    8. Millspaugh JJ
    9. McShea WJ

    (2017) Does hunting or hiking affect wildlife communities in protected areas?

    Journal of Applied Ecology 54:242–252.

    https://doi.org/10.1111/1365-2664.12700

    • Google Scholar
21. 1. Kays R
    2. Tilak S
    3. Kranstauber B
    4. Jansen PA
    5. Carbone C
    6. Rowcliffe MJ
    7. He Z

    (2010)

    Monitoring wild animal communities with arrays of motion sensitive Camera traps

    arXiv.

    • Google Scholar
22. 1. Keast A

    (1995) Habitat loss and species loss: the birds of Sydney 50 years ago and now

    Australian Zoologist 30:3–25.

    https://doi.org/10.7882/AZ.1995.002

    • Google Scholar
23. Book

    1. Kery M
    2. Schaub M

    (2012)

    Bayesian Population Analysis Using Win-BUGS: A Hierarchical Perspective

    Waltham: Academic Press.

    • Google Scholar
24. Book

    1. Kéry M
    2. Royle JA

    (2015)

    Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 1: Prelude and Static Models

    Amsterdam: Academic Press.

    • Google Scholar
25. 1. LaPoint S
    2. Gallery P
    3. Wikelski M
    4. Kays R

    (2013) Animal behavior, cost-based corridor models, and real corridors

    Landscape Ecology 28:1615–1630.

    https://doi.org/10.1007/s10980-013-9910-0

    • Google Scholar
26. 1. Lariviere S
    2. Pasitschniak-Arts M

    (1996) Vulpes vulpes

    Mammalian Species 537:1–11.

    https://doi.org/10.2307/3504236

    • Google Scholar
27. 1. Lariviere S
    2. Walton LR

    (1997) Lynx rufus

    Mammalian Species 563:1–8.

    https://doi.org/10.2307/3504533

    • Google Scholar
28. 1. Lombardi JV
    2. Comer CE
    3. Scognamillo DG
    4. Conway WC

    (2017) Coyote, fox, and bobcat response to anthropogenic and natural landscape features in a small urban area

    Urban Ecosystems 20:1239–1248.

    https://doi.org/10.1007/s11252-017-0676-z

    • Google Scholar
29. 1. Lunn D
    2. Spiegelhalter D
    3. Thomas A
    4. Best N

    (2009) The BUGS project: Evolution, critique and future directions

    Statistics in Medicine 28:3049–3067.

    https://doi.org/10.1002/sim.3680

    • PubMed
    • Google Scholar
30. 1. MacKenzie DI
    2. Nichols JD
    3. Lachman GB
    4. Droege S
    5. Andrew Royle J
    6. Langtimm CA

    (2002) Estimating site occupancy rates when detection probabilities are less than one

    Ecology 83:2248–2255.

    https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2

    • Google Scholar
31. 1. Matthies SA
    2. Rüter S
    3. Schaarschmidt F
    4. Prasse R

    (2017) Determinants of species richness within and across taxonomic groups in urban green spaces

    Urban Ecosystems 20:897–909.

    https://doi.org/10.1007/s11252-017-0642-9

    • Google Scholar
32. 1. McKinney ML

    (2006) Urbanization as a major cause of biotic homogenization

    Biological Conservation 127:247–260.

    https://doi.org/10.1016/j.biocon.2005.09.005

    • Google Scholar
33. 1. McKinney ML

    (2008) Effects of urbanization on species richness: A review of plants and animals

    Urban Ecosystems 11:161–176.

    https://doi.org/10.1007/s11252-007-0045-4

    • Google Scholar
34. 1. McShea WJ
    2. Forrester T
    3. Costello R
    4. He Z
    5. Kays R

    (2016) Volunteer-run cameras as distributed sensors for macrosystem mammal research

    Landscape Ecology 31:55–66.

    https://doi.org/10.1007/s10980-015-0262-9

    • Google Scholar
35. 1. North Carolina Wildlife Resources Commission

    (2011)

    North Carolina Black Bear Managementplan 2012-2022

    North Carolina Black Bear Managementplan 2012-2022.

    • Google Scholar
36. 1. Ordeñana MA
    2. Crooks KR
    3. Boydston EE
    4. Fisher RN
    5. Lyren LM
    6. Siudyla S
    7. Haas CD
    8. Harris S
    9. Hathaway SA
    10. Turschak GM
    11. Miles AK
    12. Van Vuren DH

    (2010) Effects of urbanization on carnivore species distribution and richness

    Journal of Mammalogy 91:1322–1331.

    https://doi.org/10.1644/09-MAMM-A-312.1

    • Google Scholar
37. 1. Parsons AW
    2. Bland C
    3. Forrester T
    4. Baker-Whatton MC
    5. Schuttler SG
    6. McShea WJ
    7. Costello R
    8. Kays R

    (2016) The ecological impact of humans and dogs on wildlife in protected areas in eastern North America

    Biological Conservation 203:75–88.

    https://doi.org/10.1016/j.biocon.2016.09.001

    • Google Scholar
38. Software

    1. R Development Core Team.

    (2008)

    R: A language and environment for statistical computing, version 3.4.0

    R Foundation for Statistical Computing, Vienna, Austria.
39. 1. Rich LN
    2. Davis CL
    3. Farris ZJ
    4. Miller DAW
    5. Tucker JM
    6. Hamel S
    7. Farhadinia MS
    8. Steenweg R
    9. Di Bitetti MS
    10. Thapa K
    11. Kane MD
    12. Sunarto S
    13. Robinson NP
    14. Paviolo A
    15. Cruz P
    16. Martins Q
    17. Gholikhani N
    18. Taktehrani A
    19. Whittington J
    20. Widodo FA
    21. Yoccoz NG
    22. Wultsch C
    23. Harmsen BJ
    24. Kelly MJ

    (2017) Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys

    Global Ecology and Biogeography 26:918–929.

    https://doi.org/10.1111/geb.12600

    • Google Scholar
40. 1. Riley SP
    2. Serieys LE
    3. Pollinger JP
    4. Sikich JA
    5. Dalbeck L
    6. Wayne RK
    7. Ernest HB

    (2014) Individual behaviors dominate the dynamics of an urban mountain lion population isolated by roads

    Current Biology 24:1989–1994.

    https://doi.org/10.1016/j.cub.2014.07.029

    • PubMed
    • Google Scholar
41. 1. Rota CT
    2. Ferreira MAR
    3. Kays RW
    4. Forrester TD
    5. Kalies EL
    6. McShea WJ
    7. Parsons AW
    8. Millspaugh JJ
    9. Warton D

    (2016) A multispecies occupancy model for two or more interacting species

    Methods in Ecology and Evolution 7:1164–1173.

    https://doi.org/10.1111/2041-210X.12587

    • Google Scholar
42. 1. Rowcliffe JM
    2. Field J
    3. Turvey ST
    4. Carbone C

    (2008) Estimating animal density using camera traps without the need for individual recognition

    Journal of Applied Ecology 45:1228–1236.

    https://doi.org/10.1111/j.1365-2664.2008.01473.x

    • Google Scholar
43. Software

    1. RStudio Team

    (2015)

    RStudio: Integrated Development for R, version 1.0.143

    RStudio, Inc, Boston.
44. 1. Saito M
    2. Koike F

    (2013) Distribution of wild mammal assemblages along an urban-rural-forest landscape gradient in warm-temperate east asia

    PLoS ONE 8:e65464.

    https://doi.org/10.1371/journal.pone.0065464

    • PubMed
    • Google Scholar
45. 1. Stan Development Team

    (2015)

    Stan: a C++ library for probability and sampling

    Stan: a C++ library for probability and sampling.

    • Google Scholar
46. 1. Strohbach MW
    2. Hrycyna A
    3. Warren PS

    (2014) 150 years of changes in bird life in Cambridge, Massachusetts from 1860 to 2012

    The Wilson Journal of Ornithology 126:192–206.

    https://doi.org/10.1676/13-127.1

    • Google Scholar
47. 1. Sturtz S
    2. Ligges U
    3. Gelman A

    (2005)

    R2WinBUGS: a package for running WinBUGS from R

    Journal of Statistical Software 12:1–16.

    • Google Scholar
48. 1. Stan Development Team

    (2015)

    Rstan: the R interface to Stan

    Rstan: the R interface to Stan.

    • Google Scholar
49. 1. Tigas LA
    2. Van Vuren DH
    3. Sauvajot RM

    (2002) Behavioral responses of bobcats and coyotes to habitat fragmentation and corridors in an urban environment

    Biological Conservation 108:299–306.

    https://doi.org/10.1016/S0006-3207(02)00120-9

    • Google Scholar
50. 1. Vogt P
    2. Riitters KH
    3. Estreguil C
    4. Kozak J
    5. Wade TG
    6. Wickham JD

    (2007) Mapping spatial patterns with morphological image processing

    Landscape Ecology 22:171–177.

    https://doi.org/10.1007/s10980-006-9013-2

    • Google Scholar
51. 1. Wang Y
    2. Smith JA
    3. Wilmers CC

    (2017) Residential development alters behavior, movement, and energetics in an apex predator, the puma

    PLoS One 12:e0184687.

    https://doi.org/10.1371/journal.pone.0184687

    • PubMed
    • Google Scholar
52. 1. Witte R
    2. Diesing D
    3. Godde M

    (1982)

    Urbanophobe, urbanoneutral, urbanophile – behavior of species concerning the urban habitat

    Flora : The Journal of Infectious Diseses and Clinical Microbiology = Infeksiyon Hastalıkları Ve Klinik Mikrobiyoloji Dergisi 177:265–282.

    • Google Scholar

Thank you for submitting your article "Wild suburbia: mammal communities are larger and more diverse in moderately developed areas" for consideration by eLife. Your article has been reviewed by four peer reviewers, including Bernhard Schmid as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Ian Baldwin as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Marc Kéry (Reviewer #3) and Ingolf Kühn (Reviewer #4).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The value of this paper is that it addresses a question of broad relevance with novel methods. The question is whether human population density near two US cities reduces the occurrence of large mammals in four types of habitats, large forests, forest fragments, open areas and yards. The assessment method involved 557 citizens operating cameras deployed for around 20 days at each of 1427 locations throughout a period of 4 years. In addition, the authors compared the results of this assessment with a global data set.

The result was that mammal occurrence did not decline with human population density and even showed an opposite tendency, except for bobcats. Even though this results is not "strong" in the sense that the main point is the absence of a negative influence of human population density, it does send a very important message, in particular because it is based on such a large sampling scheme with a clear design of five human population density levels (wild < rural < exurban < suburban < urban) factorially crossed with the four habitat types (obviously, these two factors were not fully orthogonal because for example open areas and yards could not be found in the wild).

Essential revisions:

The interpretation of human population density as equivalent to disturbance is less important than implied by the authors and should be better justified. In particular, the comparison with the so-called intermediate disturbance hypothesis at the start of the Results and Discussion section seems unnecessary and unjustified, because it is not really tested later on nor even discussed. In fact, the curves drawn in Figure 1 are not really hump-shaped and probably do not have significant downwards curvature at high human population density (interpreted as high disturbance). The hump is even more elusive if Figure 3 is inspected.

Currently, the discussion is somewhat US-centered. Although the two case-study cities are from that region, it would make the paper more relevant if comparisons with literature from other regions, including Europe, and involving other taxonomic groups would be included. In this context it would also be important to put the levels of human population densities in the two studied cities in relation to cities in the old world, which often occupy much smaller areas.

The major issue you need to address in the revision is the description of methods. The detailed comments from the reviewers in this regard are listed below:

I am not sure whether the results are influenced by a measurement bias, i.e. species tending to have higher activity in more disturbed regions and hence being more frequently recorded.

Subsection “Detection rate models”: Either refer from here to the last paragraph of the “Occupancy models” subsection or mention already here how large (long) the burn-in phase was and report the value of i.

Subsection “Occupancy models”: To me it is not clear which species were paired: all ones, or just predators?

Subsection “Occupancy models”: Explain the elements of the formula (including subscripts).

Subsection “Comparison with global occupancy data”: Be explicit on how this was assessed.

Currently, the information about the study design is placed in different parts of the manuscript so that it is difficult to get the overview. Supplementary file 1 is the most useful in this context, even though it would be easier if number of camera locations would also be shown (in addition to number of nights). It is not clear, if there was an additional spatial stratification that was not used in the analyses. For example, I could imagine that within each cell in Supplementary file 1 there were some spatial units and within these spatial units camera locations. Even if that was not the case, the question remains if spatial distance and arrangement of camera locations should have been considered.

Why did you use the crude models of rarefaction rather than obtain the inferences on species richness (including species accumulation) from the multi-species occupancy model of Rota et al.? See Dorazio et al. (2006) for how this can be done. Also, please cite the important work of Dorazio and Royle (2005) and Dorazio et al. (2006) as a foundation on which models such as that by Rota et al. are built.

- Subsection “Model covariates” the sentence “We represented whether a site allowed hunting or not using 0/1.” sounds strange, rewrite.

- Subsection “Model covariates”: Please explain what a restrictive prior is.

- Subsection “Occupancy models”: Really, the other basic groundstone on which your work is based are the multispecies occupancy models of Dorazio and Royle and of Gelfand (independently developed in 2005). These should also be cited.

- Subsection “Occupancy models”: “We assumed all species occurred independently…”. Does this fit with what was just said “It contains single-species (first order)…”?

Subsection “Data reporting”: The first part of this paragraph is a bit weird to me. If you have to have a formal paragraph like this for the journal specifications, please ignore. As of now it reads like you are trying to convince the reader why an observational study was chosen instead of a true experiment. I think it is given that a camera trap study is going to be more of an observational study – we just try to standardize our sampling design as much as possible. If this paragraph is not required, I would just go straight into your methods – i.e. study design, site selection, sample size, etc.

Subsection “Data reporting”: How did you randomize your sites? Did you use GIS and place random points within some bounds of a polygon (e.g. yard or forest preserve)? Did you place a grid across the city and chose sites closes to a random intersect? Please be more specific.

Subsection “Citizen science camera trap surveys”: Please report the mean number of cameras deployed per volunteer (as an aside, please be consistent with the use of volunteer and citizen scientist).

Subsection “Citizen science camera trap surveys”: 200 m seems close together for mammal occupancy. Please justify the independence between sites, or, consider changing your terminology from true occupancy and maybe use habitat use instead.

Subsection “Citizen science camera trap surveys”: Were cameras rotated continuously throughout the year or did sampling only occur during a particular season. Please clarify? If they were rotated throughout the year, did you revisit sites or are some sites sampled in the earlier part of the calendar year being compared to sites sample later (let’s say early spring vs. late fall). If this is the case I would be worried that the sampling periods would violate the assumption of closure for your occupancy models. Please clarify and or address the issue.

Throughout the statistical analyses: It is clear that multiple authors wrote the different sections. Please take the time to be consistent in your tone, terminology, and methods descriptions throughout. For, example R is cited 3 different ways.

Subsection “Model covariates”: Please justify why you decided to use a single season occupancy model for multiple seasons with a year covariate instead of a dynamic occupancy model. The way you did it is not wrong but does not consider the dynamics between years. If you are violating closer (see comment above), you could sub-divide your data into appropriate seasons (considering closure) and use a multi-season occupancy model instead.

Subsection “Model covariates”: Please specify if you used NLCD land use data set and used an open space category or used the NLCD canopy cover dataset.

Subsection “Model covariates”: I am a bit confused here. So, you used the percentage of forested area within the 5 km buffer that was at least connected to a large continuous forest patch larger than 1km? Could that linkage continue outside the patch? For example, would a small 5m² patch on the edge of the buffer be counted if it were connected to a larger forest patch that continues outside the buffer? It’s just a bit confusing, please clarify.

Subsection “Model covariates”: Also confused here. So now you measured the percentage of forest cover within a 100m radius? But it didn't have to be connected to continuous forest cover? So, a small vacant lot with some trees would be counted?

Subsection “Model covariates”: I have concerns about using number of detections/trap night as a metric of prey abundance (even just relative abundance). In our system, we get rabbits just sitting in front of the camera all day. How can you tease apart that a single rabbit didn't sit in front of the camera continuously triggering it at one site, but not another. For example, what if in a single night one rabbit sat in front of a camera for 100 minutes (100 detections) at site A and 100 rabbits passed in front of the camera a single time (100 detections) at site B? Very different abundance, but you get the same answer.

Subsection “Model covariates”: Does NDVI get at understory? Will a cell with really dense canopy cover but no understory give a different value than one with really dense canopy cover and thick understory? From what I have experienced, dense canopy cover in an urban park (with no understory) will often show up the same as a forested area if there are few gaps in the tree.

Subsection “Model covariates”: Did you use monthly NDVI, did you pick a month with peak greenness, or did you average across the month? I am assuming you used one value since you did not use a dynamic occupancy model, but please clarify.

Subsection “Model covariates”: Indicate which camera model was the reference.

Subsection “Detection rate models”: Did you have adequate fit for both your Poisson and occupancy models? Figure 4—figure supplement 1 has huge error bars, which could be a result of over parameterizing the model. Maybe report results from PPC in supplemental table. However, PPC checks for occupancy models are tough as they have trouble accounting for the correcting of detection. Cross validation is probably best. See Hooten and Hobbs (2015).

Subsection “Occupancy models”: I think you need to mention that you ran an occupancy model for a subset of species (and mention species) somewhere here. Unless you ran a multi-species model for all the species, in which case I don't see any mention of the rest of the species in the Results section.

Subsection “Occupancy models”: I am assuming that the city indicator is a 1 or a 0 based on your table in supplemental material, but this is not clear in the text. Please clarify how that interaction is formulated. You also need to report which city is represented by 1 and which city is represented by 0. Without this your tables don't mean much. You also need to report the intercept value of your model in your supplemental tables, so the reader can interpret results for the reference city.

Subsection “Comparison with global occupancy data”: Here is where the global comparison is first mentioned. I think you need a justification and a bit of a background in the introduction. Until I got to the Discussion section and saw the results, I was left wondering what the point of this analysis was.

Subsection “Study sites”: This passage is written a bit unclearly. As is, it sounds like you deliberately avoided urban areas in both cities. I suggest rephrasing for clarity.

Subsection “Model covariates”: I am assuming 'hunting' means hunting was allowed or hunting was recorded during the sample period? Be more specific.

Subsection “Model covariates”: I suggest putting the covariate abbreviation that you use in your models and tables in parentheses after each time they are mentioned in the text. I think this will help the reader follow along.

Subsection “Model covariates”: I suggest rephrasing to "We included an indicator (0 or 1) to categorize whether a site allowed hunting or not." Also, I assume that 0 indicates no hunting? But please be explicit in the text.

Subsection “Detection rate models”: Change 'count' to Poisson.

Subsection “Detection rate models”: I think you should say what your thinning rate was instead of just ith.

Subsection “Occupancy models”: What about plot type and all the other covariates?

Summary:

The value of this paper is that it addresses a question of broad relevance with novel methods. The question is whether human population density near two US cities reduces the occurrence of large mammals in four types of habitats, large forests, forest fragments, open areas and yards. The assessment method involved 557 citizens operating cameras deployed for around 20 days at each of 1427 locations throughout a period of 4 years. In addition, the authors compared the results of this assessment with a global data set.

The result was that mammal occurrence did not decline with human population density and even showed an opposite tendency, except for bobcats. Even though this results is not "strong" in the sense that the main point is the absence of a negative influence of human population density, it does send a very important message, in particular because it is based on such a large sampling scheme with a clear design of five human population density levels (wild < rural < exurban < suburban < urban) factorially crossed with the four habitat types (obviously, these two factors were not fully orthogonal because for example open areas and yards could not be found in the wild).

Essential revisions:

The interpretation of human population density as equivalent to disturbance is less important than implied by the authors and should be better justified. In particular, the comparison with the so-called intermediate disturbance hypothesis at the start of the Results and Discussion section seems unnecessary and unjustified, because it is not really tested later on nor even discussed. In fact, the curves drawn in Figure 1 are not really hump-shaped and probably do not have significant downwards curvature at high human population density (interpreted as high disturbance). The hump is even more elusive if Figure 3 is inspected.

We have removed the mention of IDH.

Currently, the discussion is somewhat US-centered. Although the two case-study cities are from that region, it would make the paper more relevant if comparisons with literature from other regions, including Europe, and involving other taxonomic groups would be included. In this context it would also be important to put the levels of human population densities in the two studied cities in relation to cities in the old world, which often occupy much smaller areas.

We have attempted to make comparisons with other cities around the world and added one example of other taxa (Results and Discussion section): “Our discovery of a wild suburbia suggests high levels of adaptation by mammals to developed landscapes over the last few decades, including predators and prey. […] Further understanding of how urban wildlife navigates human-dominated areas and factors that contribute to the adaptation of species to the Anthropocene will be critical to maintaining diversity in a rapidly urbanizing world.”

We hesitate to add too much on other taxa to the discussion for the sake of brevity and because the data we collected are relevant to mammals. The body of literature on other taxa (in particular birds and plants) is rich and we feel that further detailed discussion of those studies is not warranted here. However, if the editor feels strongly we will add a more in-depth discussion to that point.

The major issue you need to address in the revision is the description of methods. The detailed comments from the reviewers in this regard are listed below:

I am not sure whether the results are influenced by a measurement bias, i.e. species tending to have higher activity in more disturbed regions and hence being more frequently recorded.

We would argue that higher activity indicates higher use of a particular habitat which is exactly what we are trying to show. We make no claims to density or absolute abundance, which would indeed be tricky due to differences in activity between gradient levels. What we are measuring (with occupancy and relative abundance) is essentially relative use between gradient levels, arguably a habitat preference.

Subsection “Detection rate models”: Either refer from here to the last paragraph of the “Occupancy models” subsection or mention already here how large (long) the burn-in phase was and report the value of i.

We have added this information and simplified the presentation “We based inference on posterior samples generated from three Markov chains, using trace plots to determine an adequate burn-in phase. All models achieved adequate convergence (${\displaystyle \hat{R}\le 1.1}$) (Gelman et al., 2014) by running for 50,000 iterations following a burn-in phase of 1000 iterations, thinning every 10 iterations.”

Subsection “Occupancy models”: To me it is not clear which species were paired: all ones, or just predators?

We have clarified this “We used the multispecies occupancy model of Rota et al., (2016), a generalization of the single-season occupancy model (MacKenzie et al., 2002) to accommodate two or more interacting species, to model the occupancy of four predator species: bobcat, coyote, red fox and gray fox.”

Subsection “Occupancy models”: Explain the elements of the formula (including subscripts).

We have added “Where${\displaystyle {\psi }_{11}}$ is the probability that both species occupy a site, ${\displaystyle {\psi }_{10}}$ is the probability that only species 1 occupies a site,${\displaystyle {\psi }_{01}}$ is the probability that only species 2 occupies a site and ${\displaystyle {\psi }_{00}}$ is the probability that neither species occupies a site.”

Subsection “Comparison with global occupancy data”: Be explicit on how this was assessed.

We have added clarification to this point “We summarized occupancy estimates of Rich et al. and our own study within each developmental level using a box and whisker plot and assessed statistically significant differences based on whether or not interquartile ranges overlapped.”

Currently, the information about the study design is placed in different parts of the manuscript so that it is difficult to get the overview. Supplementary file 1 is the most useful in this context, even though it would be easier if number of camera locations would also be shown (in addition to number of nights). It is not clear, if there was an additional spatial stratification that was not used in the analyses. For example, I could imagine that within each cell in Supplementary file 1 there were some spatial units and within these spatial units camera locations. Even if that was not the case, the question remains if spatial distance and arrangement of camera locations should have been considered.

Spatial replicates are indeed included in Supplementary file 1, in parentheses next to the camera nights. We also set a minimum spatial distance as part of our design (subsection “Citizen science camera trap surveys”) “All adjacent cameras were spaced at least 200m apart”.

Why did you use the crude models of rarefaction rather than obtain the inferences on species richness (including species accumulation) from the multi-species occupancy model of Rota et al.? See Dorazio et al. (2006) for how this can be done. Also, please cite the important work of Dorazio and Royle (2005) and Dorazio et al. (2006) as a the foundation on which models such as that by Rota et al. are built.

We suspect that the term “multispecies model” is a source of confusion. The “multispecies” model of Rota et al. is not a community model in the sense of Dorazio et al. (2006) and Dorazio and Royle (2005), but rather an extension of the single-species, single-season occupancy models of Mackenzie et al. (2002) and co-occurrence models of Richmond et al. 2010, with no community-level attribute estimators or focus, no species-level effects. We have clarified this in the Materials and methods section: “We used the multispecies occupancy model of Rota et al. (2016) to model the occupancy of four predator species: bobcat, coyote, red fox and gray fox. This model is distinct from the classic multispecies community models (Dorazio and Royle, 2005; Dorazio et al., 2006; Gelfand et al., 2005) and is rather a generalization of the single-season occupancy model (MacKenzie et al., 2002) to accommodate two or more interacting species.”

While we could formulate the model in a similar way to Dorazio/Royle community models, adding hyperparameters for richness, we think the rarefaction estimates are adequate for the story we are telling.

- Subsection “Model covariates” the sentence “We represented whether a site allowed hunting or not using 0/1.” sounds strange, rewrite.

We have changed this to “We included an indicator (0/1, no hunting/hunting) to categorize whether a site allowed hunting or not.”

- Subsection “Model covariates”: Please explain what a restrictive prior is.

We have added clarification: “We diagnosed univariate correlations between covariates using a Pearson correlation matrix, and used a restrictive prior for beta coefficients where correlation was >0.60 (i.e. logistic(0,1); a prior with reduced variance to induce shrinkage, similar to ridge regression; Hooten and Hobbs, 2015). All covariates were mean-centered.”

- Subsection “Occupancy models”: Really, the other basic groundstone on which your work is based are the multispecies occupancy models of Dorazio and Royle and of Gelfand (independently developed in 2005). These should also be cited.

We suspect that the term “multispecies model” is a source of confusion. The “multispecies” model of Rota et al., is not a community model in the sense of Dorazio and Royle and Gelfand, but rather an extension of the single-species, single-season occupancy models of Mackenzie et al., (2002) and co-occurrence models of Richmond et al., 2010, with no community-level attribute estimators or focus, no species-level effects. We have clarified this in the Materials and methods section: “We used the multispecies occupancy model of Rota et al. (2016) to model the occupancy of four predator species: bobcat, coyote, red fox and gray fox. This model is distinct from the classic multispecies community models (Dorazio and Royle, 2005; Dorazio et al., 2006; Gelfand et al., 2005) and is rather a generalization of the single-season occupancy model (MacKenzie et al., 2002) to accommodate two or more interacting species.”

- Subsection “Occupancy models”: “We assumed all species occurred independently…”. Does this fit with what was just said “It contains single-species (first order)…”?

We suspect this again is a product of some confusion over the model and the fact that it operates differently than the classic multispecies community models.

Subsection “Data reporting”: The first part of this paragraph is a bit weird to me. If you have to have a formal paragraph like this for the journal specifications, please ignore. As of now it reads like you are trying to convince the reader why an observational study was chosen instead of a true experiment. I think it is given that a camera trap study is going to be more of an observational study – we just try to standardize our sampling design as much as possible. If this paragraph is not required, I would just go straight into your methods – i.e. study design, site selection, sample size, etc.

While much of this information is required by the journal, we were able to pare it down and move it to other sections of the Materials and methods section, thus eliminating this first paragraph as the reviewer suggested.

Subsection “Data reporting”: How did you randomize your sites? Did you use GIS and place random points within some bounds of a polygon (e.g. yard or forest preserve)? Did you place a grid across the city and chose sites closes to a random intersect? Please be more specific.

We have clarified this: “Camera placement was randomized as much as possible using ArcMap (Version 10.1) to randomly generate points within polygons while following certain rules. For example, we selected sites within forests that volunteers were permitted to access and were within a reasonable hiking distance (i.e. < 11km hike round trip) with terrain that was not too steep to traverse safely (i.e. <45 degree slope). Within yards, cameras were placed as randomly as possible while avoiding the highest human traffic areas (i.e. walkways, doors, gates and driveways).”

Subsection “Citizen science camera trap surveys”: Please report the mean number of cameras deployed per volunteer (as an aside, please be consistent with the use of volunteer and citizen scientist).

We have clarified the instances of “citizen scientists” to “citizen scientist volunteers” to maintain consistency but also bring attention to the nature of those volunteers in a couple of key places. We have added: “Each individual camera was considered a “camera site” and volunteers ran cameras at an average of 2 sites each.”

Subsection “Citizen science camera trap surveys”: 200 m seems close together for mammal occupancy. Please justify the independence between sites, or, consider changing your terminology from true occupancy and maybe use habitat use instead.

We agree with the reviewer that this is not classical occupancy, in fact, all camera trap based studies violate the closure and independence assumptions since they are merely measuring the passage of animals in front, and not the longer term occupancy of a patch (Burton et al., 2015; Efford and Dawson, 2012). We have added: “Although we are using the term occupancy, because data were collected from camera traps estimates are more analogous to “use” than true occupancy (Burton et al., 2015).” We think this is sufficient to address the point and have left the terminology throughout as “occupancy”. We think this is a more established term than “use” and thus less confusing, however if the reviewer feels strongly we can change the terminology throughout.

Subsection “Citizen science camera trap surveys”: Were cameras rotated continuously throughout the year or did sampling only occur during a particular season. Please clarify? If they were rotated throughout the year, did you revisit sites or are some sites sampled in the earlier part of the calendar year being compared to sites sample later (let’s say early spring vs. late fall). If this is the case I would be worried that the sampling periods would violate the assumption of closure for your occupancy models. Please clarify and or address the issue.

We never revisited sites. Some sites are from the earlier part of the year, some from later with at least some samples within each season but not necessarily balanced. We have clarified this: “Cameras were deployed for three weeks and then moved to a new location without returning, with sampling taking place continuously throughout the year.”

Unfortunately, as is the nature of camera trapping, closure has already been violated and we have tried to clarify that in the Materials and methods section: “Although we are using the term occupancy, because data were collected from camera traps estimates are more analogous to “use” than true occupancy (Burton et al., 2015).”

With respect to temporal closure, it seems like we would expect occupancy estimates to be biased high as a result, but since our interest is more in relative occupancy (or use in this case) along the gradient, we believe that bias has little effect on our conclusions.

Throughout the statistical analyses: It is clear that multiple authors wrote the different sections. Please take the time to be consistent in your tone, terminology, and methods descriptions throughout. For, example R is cited 3 different ways.

We have made an effort to unify the tone and terminology throughout the methods.

Subsection “Model covariates”: Please justify why you decided to use a single season occupancy model for multiple seasons with a year covariate instead of a dynamic occupancy model. The way you did it is not wrong but does not consider the dynamics between years. If you are violating closer (see comment above), you could sub-divide your data into appropriate seasons (considering closure) and use a multi-season occupancy model instead.

Since we were covering such a large area and spatial variation in housing density was central to our question, we were most interested in gaining spatial replicates and less interested in temporal patterns, thus our design was such that cameras were not left in one place more than a month and therefore, to our knowledge, a dynamic occupancy model was not appropriate for this analysis.

Subsection “Model covariates”: Please specify if you used NLCD land use data set and used an open space category or used the NLCD canopy cover dataset.

We have clarified this “We used the Landscape Fragmentation Tool v2.0 (Vogt et al., 2007) and the NLCD (2006) land use dataset in ArcMap (Version 10.1) to create a landcover layer representing the percent of large core forest (forest patches larger than 1km²) in a 5km radius around camera locations.”

Subsection “Model covariates”: I am a bit confused here. So, you used the percentage of forested area within the 5 km buffer that was at least connected to a large continuous forest patch larger than 1km? Could that linkage continue outside the patch? For example, would a small 5m² patch on the edge of the buffer be counted if it were connected to a larger forest patch that continues outside the buffer? It’s just a bit confusing, please clarify.

We have clarified this: “Forest patches did not necessarily fall entirely within the buffer.”

Subsection “Model covariates”: Also confused here. So now you measured the percentage of forest cover within a 100m radius? But it didn't have to be connected to continuous forest cover? So, a small vacant lot with some trees would be counted?

Yes indeed, the reviewer is correct that our intention was to take into account the small patches of habitat present in the surburban matrix, at fairly fine scales, which we surmised would be particularly important for shy species to navigate the urban/suburban zones. We added that clarification as well: “We used a 100m radius for small scale forest cover to best represent small forest patches within suburban neighborhoods (e.g. small vacant lots with trees, greenways).”

Subsection “Model covariates”: I have concerns about using number of detections/trap night as a metric of prey abundance (even just relative abundance). In our system, we get rabbits just sitting in front of the camera all day. How can you tease apart that a single rabbit didn't sit in front of the camera continuously triggering it at one site, but not another. For example, what if in a single night one rabbit sat in front of a camera for 100 minutes (100 detections) at site A and 100 rabbits passed in front of the camera a single time (100 detections) at site B? Very different abundance, but you get the same answer.

We agree with the reviewer that use of detections/day can be a problematic index of relative abundance, especially where individual photos are considered. To deal with this we considered only photographs separated by more than 1 minute as temporally independent records (see Parsons et al., 2016, for an analysis of the adequacy of this 1 minute interval for independence). Thus, if a rabbit sat in front of the camera for 100 minutes, it would still be counted as 1 rabbit, not 100. If 100 rabbits passed in front of the camera, assuming they did not pass in continuous succession without periods of time between of at least 1 minute, they would be counted as 100 rabbits. We have included this language in the Materials and methods section: “We grouped consecutive photos into on sequence if they were <60 seconds apart, and used these sequences as independent records, counting animals in the sequence, not individual photos (Parsons et al., 2016).”

Subsection “Model covariates”: Does NDVI get at understory? Will a cell with really dense canopy cover but no understory give a different value than one with really dense canopy cover and thick understory? From what I have experienced, dense canopy cover in an urban park (with no understory) will often show up the same as a forested area if there are few gaps in the tree.

As the reviewer points out, NDVI will not always reliably reflect understory, especially where canopy cover is dense. However, we felt that this was the best remote-sensing measurement available to try and account for this, however we also measured detection distance on the ground which accounts for thick undergrowth as well as the unique topography of each site which influenced detection probability. We have clarified this in the Materials and methods section: “To complement NDVI, we also considered site-specific detection distance, a measure of how far away the camera was able to detect a human, which is influenced by both understory and site topography.”

Subsection “Model covariates”: Did you use monthly NDVI, did you pick a month with peak greenness, or did you average across the month? I am assuming you used one value since you did not use a dynamic occupancy model, but please clarify.

We have added some clarification “Because both ambient temperature and undergrowth can affect the camera’s ability to detect an animal, we included daily covariates for temperature and NDVI (Moderate Resolution Imaging Land Terra Vegetation Indices 1km monthly, an average value over the month(s) the camera ran) obtained from Env-DATA (Dodge et al., 2013).”

Subsection “Model covariates”: Indicate which camera model was the reference.

We have added this clarification: “In Raleigh, two different camera models were used (both Reconyx and Bushnell) so we added a 0/1 (Bushnell/Reconyx) covariate to account for potential difference in detection probability between the two brands.”

Subsection “Detection rate models”: Did you have adequate fit for both your Poisson and occupancy models? Figure 4—figure supplement 1 has huge error bars, which could be a result of over parameterizing the model. Maybe report results from PPC in supplemental table. However, PPC checks for occupancy models are tough as they have trouble accounting for the correcting of detection. Cross validation is probably best. See Hooten and Hobbs (2015).

We have added the results of goodness-of-fit tests as a supplement (Supplementary file 3), using PPC for both. Since current methods of PPC with occupancy models have difficulty handling detection, as the reviewer points out, we derived a novel (to our knowledge) method for posterior predictive checks of the occupancy model and have included that proof as Supplementary file 4. We have added this to the Materials and methods section: “We assessed model fit with posterior predictive checks (PPC) (Gelman et al., 2014; Kery and Schaub, 2012) by calculating the sum of squared Pearson residuals from observed data (T(y)) and from data simulated assuming the fully parameterized model was the data-generating model (T(y_sim)). We calculated a Bayesian p-value as p_B = Pr(T(y_sim) > T(y)) from posterior simulations and assumed adequate fit if 0.1 < p_B < 0.9. To our knowledge, the squared Pearson’s residual has not been derived in the context of occupancy models, so we present our derivation of this test statistic in Supplementary file 4. We added a random effect on detection/non-detection for the coyote portion of the model since initial assessments of fit for this species were inadequate (i.e. p_B>0.9).”

Subsection “Detection rate models”: “Both of our models fit well, with Bayesian p-values between 0.1 and 0.9 (Supplementary file 3).” The additional random effect placed on coyote detection/non-detection to improve fit did not substantially change estimates of psi or associated uncertainty.

Subsection “Occupancy models”: I think you need to mention that you ran an occupancy model for a subset of species (and mention species) somewhere here. Unless you ran a multi-species model for all the species, in which case I don't see any mention of the rest of the species in the Results section.

We have added: “We used the multispecies occupancy model of Rota et al., (2016) to estimate the probability of occupancy of four predator species: bobcat, coyote, red fox and gray fox.”

Subsection “Occupancy models”: I am assuming that the city indicator is a 1 or a 0 based on your table in supplemental material, but this is not clear in the text. Please clarify how that interaction is formulated. You also need to report which city is represented by 1 and which city is represented by 0. Without this your tables don't mean much. You also need to report the intercept value of your model in your supplemental tables, so the reader can interpret results for the reference city.

We have added that clarification: “We considered interactions (i.e. city*covariate) between each occupancy covariate and city (0/1, DC/Raleigh).”

We have also added the intercepts into Supplementary file 5, where they were missing.

Subsection “Comparison with global occupancy data”: Here is where the global comparison is first mentioned. I think you need a justification and a bit of a background in the introduction. Until I got to the Discussion section and saw the results, I was left wondering what the point of this analysis was.

We have attempted to add some more clarity of structure and purpose to the end of the Introduction, including about our comparison with global wildlands: “Our objectives were to determine how diversity, richness, detection rate, and occupancy of the mammal community changes as a function of human disturbance. We hypothesized that the availability of supplemental food at higher levels of development would positively affect mammalian populations and outweigh the negative effects of disturbance, except for the most sensitive species. Specifically, we predicted that relative abundance and occupancy of mammals would increase with developmental level but that species richness and diversity at these higher levels would be lower. Furthermore, we predicted that the occupancy of the most sensitive mammal species (i.e. large and medium carnivores) would be lower at the highest development levels compared to wild areas both in our study area and around the world.”

Subsection “Study sites”: This passage is written a bit unclearly. As is, it sounds like you deliberately avoided urban areas in both cities. I suggest rephrasing for clarity.

We have rephrased to clarify: “Washington, District of Columbia, USA (hereafter DC) is a city of approximately 177km² with an estimated human population size of 681,000, thus a density of 3,847 people/km². Our study spanned a 56,023.7km² area around the city with a mean of 4.4 houses/km² and matrix of agriculture (~21.3%) and forest (~54.1%). Raleigh, North Carolina, USA (hereafter Raleigh) is approximately 375km² with an estimated human population size of 459,000, thus a density of 1,278 people/km². Our study spanned a 66,640km² area around the city with a mean of 17.7 houses/km² and matrix of agriculture (~24.3%) and forest (~52.3%).”

Subsection “Model covariates”: I am assuming 'hunting' means hunting was allowed or hunting was recorded during the sample period? Be more specific.

We have added this clarification: “We modeled variation in occupancy (ψ) using 13 covariates (Supplementary file 2) representing development level, the amount of core forest, small scale forest cover, prey relative abundance and whether hunting was allowed.”

Subsection “Model covariates”: I suggest putting the covariate abbreviation that you use in your models and tables in parentheses after each time they are mentioned in the text. I think this will help the reader follow along.

While we agree with the reviewer in principle, we also think that this will clutter the text significantly and may reduce readability, so we have chosen not to include the abbreviations in the text.

Subsection “Model covariates”: I suggest rephrasing to "We included an indicator (0 or 1) to categorize whether a site allowed hunting or not." Also, I assume that 0 indicates no hunting? But please be explicit in the text.

We have made the suggested change and added some clarifications about the reference level: “We included an indicator (0/1, no hunting/hunting) to categorize whether a site allowed hunting or not.”

Subsection “Detection rate models”: Change 'count' to Poisson.

We feel this is not necessary since we mention above that it is a Poisson distributed count model and calling it a “count” model is statistically correct and easy to understand. If the reviewer feels strongly, however, we are willing to change it.

Subsection “Detection rate models”: I think you should say what your thinning rate was instead of just ith.

We have added more clarification: “All models achieved adequate convergence (${\displaystyle \hat{R}\le 1.1}$) (Gelman et al., 2014) by running for 50,000 iterations following a burn-in phase of 1000 iterations, thinning every 10 iterations.”

Subsection “Occupancy models”: What about plot type and all the other covariates?

We have added: “We based predictor significance on whether beta coefficient 95% credible intervals overlapped zero.”

We did not assess differences in occupancy between plot types, in part because detections were so split (i.e. basically no bobcats or coyotes in yards so difficulty with occupancy model convergence). Therefore, we felt this trend would be more easily illustrated with the count model.

Author details

1. Arielle Waldstein Parsons

   1. North Carolina Museum of Natural Sciences, Raleigh, United States
   2. Department of Forestry & Environmental Resources, North Carolina State University, Raleigh, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   arielle.parsons@naturalsciences.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1076-2896

2. Tavis Forrester

   1. Oregon Department of Fish and Wildlife, Gekeler Lane, United States
   2. Smithsonian Conservation Biology Institute, Front Royal, United State

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Megan C Baker-Whatton

   The Nature Conservancy, Fairfax Drive Arlington, Virginia

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

4. William J McShea

   Smithsonian Conservation Biology Institute, Front Royal, United State

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Christopher T Rota

   Division of Forestry and Natural Resources, Wildlife and Fisheries Resources Program, West Virginia University, Morgantown, United States

   Contribution

   Formal analysis, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

6. Stephanie G Schuttler

   North Carolina Museum of Natural Sciences, Raleigh, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Joshua J Millspaugh

   Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, College of Forestry and Conservation, University of Montana, Missoula, United States

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

8. Roland Kays

   1. North Carolina Museum of Natural Sciences, Raleigh, United States
   2. Department of Forestry & Environmental Resources, North Carolina State University, Raleigh, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

• 4,054

  Page views

• 450

  Downloads

• 45

  Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.