University of Nevada, Reno THE ROLE OF URBAN CLIMATE AND

University of Nevada, Reno
THE ROLE OF URBAN CLIMATE AND LAND COVER IN PHENOLOGY,
NEST SUCCESS, AND HABITAT USE
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in
Ecology, Evolution, and Conservation Biology
by
Miles Becker
Dr. Peter J. Weisberg/Dissertation Advisor
August 2013
UMI Number: 3595627
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UNIVERSITY OF NEVADA, RENO
THE GRADUATE SCHOOL
We recommend that the dissertation
prepared under our supervision by
MILES BECKER
entitled
The Role of Urban Climate and Land Cover in Phenology,
Nest Success, and Habitat Use
be accepted in partial fulfillment of the
requirements for the degree of
DOCTOR OF PHILOSOPHY
Peter J. Weisberg, PhD, Advisor
Scott Bassett, PhD, Committee Member
Matthew Forister, PhD, Committee Member
James Sedinger, PhD, Committee Member
Laurel Saito, PhD, Graduate School Representative
Marsha H. Read, PhD, Dean, Graduate School
August 2013
i ACKNOWLEDGEMENTS
S. Bassett, M. Forister, L. Saito, and J. Sedinger gave invaluable insights and comments
on all aspects of the project that greatly improved its quality and scope. P. Weisberg
simultaneously buoyed and grounded the project with optimism and thoughtful criticism,
skillfully adjusting focus between the trees and the forest. Numerous EECB students
reviewed and commented on the work at varying stages. Special thanks deservedly goes
to D. Nonne for patient and thorough assistance with the mechanics of nest survival
analysis. C. Taylor and M. Johnson assisted with youthful energy and attention to detail
in the field. Land cover and parcel data sets were courteously provided by the City of
Reno and Washoe County. Access to restoration sites was granted by The Nature
Conservancy. Small grants and research support were provided by the Ecology,
Evolution, and Conservation Biology Graduate Program at University of Nevada, Reno.
ii SUMMARY ABSTRACT
Urban development is the fastest growing global land use and it increasingly brings
drastic environmental change to natural systems. Climatic variability and spatial habitat
heterogeneity within a city could influence species in habitat remnants. The objectives of
this project were to investigate the relative importance of temperature variation and urban
land cover for nest survival, the function of thermal refuges for habitat use by breeding
birds, and the relationship between urban climate and phenology. Temperatures, leaf
phenology, invertebrate abundance, nest habitat, and nest fate were monitored over a
four-year study period at nine sites along an urban to rural gradient. The sites were
located in a riparian corridor passing through the cities of Reno and Sparks, Nevada.
Study area temperature anomalies from the 20-yr normal strongly impacted nest survival
rates of American Robins (Turdus migratorius), Mourning Dove (Zenaida macroura),
and Black-headed Grosbeaks (Pheucticus melanocephalus). Nest survival increased at
warmer than normal temperatures and to a lesser extent at colder than normal
temperatures. Canopy cover within a 20m radius of the nest and pedestrian traffic at a
site were positively associated with nest survival. Microsites available to nesting birds
that had more vegetation were cooler at night and warmer in the day when they had more
bare ground, less impervious surfaces, and less canopy cover. The nests of doves and
robins were constructed at cooler locations when air temperatures were warmer.
Temperature varied between sites with the warmest temperatures recorded in rural sites,
followed by the more urban sites, and with coolest temperatures in suburban sites. The
timing of nesting by robins occurred earlier in the warmer sites and at sites with earlier
bud break by Populus spp. Peaks in foliar invertebrate abundance were also weakly
iii associated with leaf out, while the sequence of peaks by aerial invertebrates was related
more to the level of urbanization at a site. The results from these studies demonstrate the
ability of urban development to differentially affect phenology across trophic levels, have
an additive effect with temperature variation on nest survival, and offer opportunities to
provide thermal refuges to buffer the impacts of regional climate change.
iv TABLE OF CONTENTS
i
ACKNOWLEDGEMENTS
iii
SUMMARY
iv
TABLE OF CONTENTS
v
LIST of TABLES
vii
LIST of FIGURES
1
PROJECT BACKGROUND
12
CHAPTER 1: THE INFLUENCE OF URBAN CLIMATE ON TREE,
INVERTEBRATE, AND NESTING PHENOLOGY
52
CHAPTER 2: SYNERGISTIC EFFECTS OF SPRING TEMPERATURES AND
LAND COVER ON NEST SURVIVAL OF URBAN BIRDS
88
CHAPTER 3: USING NEST HABITAT FOR SUITABLE MICROCLIMATES
AT WARMER TEMPERATURES
v LIST OF TABLES
p.45
TABLE 1.1 A summary of logistic exposure nest survival models for three
species along an urban to rural gradient in Reno and Sparks, Nevada. Models
include covariates and are compared to the null model of constant survival S(.).
Individual covariates included are the temperature deviation from 20-yr normal at
hatching with a quadratic term (hdev2), amount of canopy cover in a 20m radius
(canopy 20), and rates of pedestrians at a study site (peds). An interaction term
for species and nest stage (stage*species) was also included to represent three
stages of nesting that differ in daily survival rates. The lowest AICc value of all
candidate models considered was 1444.39.
p.81
TABLE 2.1 Categorical threshold values of temperatures and the timing of
breeding used to analyze nest habitat use by three bird species breeding in urban
habitat in northern Nevada. Percentages of four land cover types within a 20m
radius of nests and nest height are also reported. All values are means (µ) ± SD
for the associated sample size (N).
p.118 TABLE 3.1 Qualitative indicators of sequential stages of bud break used to
measure leaf phenology in Salix sp. and Populus sp. at nine sites on an urban to
rural gradient.
p.119 TABLE 3.2 Site level differences in environmental factors at nine sites on an
urban to rural gradient. The urban index is a PCA score derived from canopy
vi cover, impervious surface, bare ground, and road density. Estimates of relative
robin densities are in the unit of birds per hectare.
p.120 TABLE 3.3 Abundance by taxon of invertebrates captured with adhesive traps
from May to July 2011 at nine sites in northern Nevada.
vii LIST OF FIGURES
p.46
FIGURE 1.1 Study area location (star) in the western United States. Enlarged
area of Reno and Sparks, Nevada with study sites (black) connected by the
Truckee River (black line). Location of the National Weather Station is marked
by the +. Enlarged images of two study sites demonstrate the within-site variation
in land cover and among-site variation along the urban to rural gradient.
p.47
FIGURE 1.2 Weekly averages of daily maximum temperatures in the study area
during the breeding season for 2009 – 2012. Upper horizontal dashed line marks
a temperature threshold of 27 °C, the upper boundary of physiological zero.
Lower horizontal dashed line marks the temperature threshold for 15 °C, below
which nestlings are likely to be negatively impacted by cold.
p.48
FIGURE 1.3 Temperature deviation from the 20-yr norm at the time of clutch
initiation for three bird species nesting in urban habitat over a four-year study
period.
p.49
FIGURE 1.4A-B Brood parasitism of grosbeak nests by Brown-headed Cowbirds
in A) 36 grosbeak nests that either fledged young or failed, and B) the number of
cowbird eggs deposited at a range of temperature deviations from the 20-yr
normal for the five days during egg laying.
p.50
viii FIGURE 1.5 Fitted curve of nestling survival probability estimates (± SE) for
temperature deviations from normal after hatching for three species breeding in
urban habitat over a four-year study period.
p.51
FIGURE 1.6A-B Differences in the amount of canopy cover within a 20m radius,
and rates of pedestrian traffic, between the nests of three species that fledged
young or failed in urban habitat over a four-year study period.
p.82
FIGURE 2.1A - F Hypothesized effects of temperature on nest site selection.
Nest habitat on the Y axis represents % cover of one of the land cover variables
measured (bare ground, canopy cover, impervious surfaces, or understory
vegetation). Relative timing of nesting in the breeding season on the X axis
represents seasonal variation of any one of several time varying factors that have
the potential to influence nest site selection (e.g. predation risk, human activity).
Lines represent different levels to air temperature at the time of nesting (cold =
dashed grey, mild = solid grey, warm = solid black). Interpretation of the
graphical illustration is provided by the presence or absence of a significant
effect. (after Gotelli and Ellison 2004)
p.83
FIGURE 2.2 Daily variation of maximum air temperatures during spring and
summer in Reno, Nevada from 2009 to 2012.
p.84
ix FIGURE 2.3A - D The influence of land cover within 20m on Tmax (left) and
Tmin (right) recorded at 48 microsites in urban reserves from April 21 to July 13,
2011. Relative levels of land cover types are categorized into three groups of low,
mid, and high. The upper percentage threshold value of low, mid, and high levels
were 30, 55, and 88 for understory vegetation (A); 6, 24, and 75 for impervious
surfaces (B); 10, 30, and 92 for canopy cover (C); and 8, 19, and 48 for bare
ground (D).
p.86
FIGURE 2.4A - D Microhabitat used at 278 robin and dove nest sites for
different levels of the timing of breeding (early, middle, late) and the relative
daily temperature maximum in the five days before nesting (cold, mild, warm).
Temperature levels differed by 7.5 °C and timing levels differed by an average of
21 days. The four land cover variables at a nest that could have varied with time
or temperature were understory vegetation (A), impervious surfaces (B), bare
ground (C), and canopy cover (D).
p.87
FIGURE 2.5 Counts of corvids observed on transects at eight sites in a riparian
corridor of northern Nevada. Relative maximum temperature on the day of a
survey (cold, mild, warm) are compared within a site over eight survey intervals.
Each temperature level spanned a 7.5 °C increment.
p.121 FIGURE 3.1 Location of nine study sites (black) along the Truckee River (black
line) on an urban to rural gradient across the cities of Reno and Sparks, Nevada.
x p.122 FIGURE 3.2 Hypothesized pathways between urban development and
phenology. Predictor variables are labeled in boxes and response variables are
open text. Arrows indicate the direction of the relationship between predictor and
response variable.
p.123 FIGURE 3.3 Average daily temperature maximum at study sites in April and
June 2011. Sites are arranged left to right according to their respective level of
urban development.
p.124 FIGURE 3.4 The timing of bud burst in Populus sp. and Salix sp., peak
abundance for aerial, aquatic, and foliar invertebrates, and clutch initiation in
American Robin at sites across an urban to rural gradient. Sites are arranged left
to right according to their respective level of urban development.
p.125 FIGURE 3.5 Path analysis modeling hypothesized relationships between predictor
variables (in boxes) and the phenology of leaf out, peak invertebrate abundance,
and robin lay date. The relative strength of associations between variables are
indicated by line weight, based on standardized Beta coefficients. The residual
variance (error) for each response variable are given in small boxes. Darker lines
indicate a stronger relationship and dashed lines represent a negative relationship.
xi p.126 FIGURE 3.6 Maximum abundance of invertebrates reached at the timing of peak
abundance in sites on a rural to urban gradient. Invertebrate guilds were aerial,
foliar, or aquatic.
1 PROJECT BACKGROUND
The world reached a symbolic landmark in 2008 when the number of people
living in cities crept past fifty percent. As a species, more humans now live in urban
habitat than ever before. Issues of food supply, air and water quality, waste management,
energy, and livability are practical concerns for urban dwellers. Urban sustainability is
intimately tied to ecological processes – nutrient cycling, energy flow, organisms,
hydrology - and achieving sustainability will require in-depth knowledge of urban
ecosystem function.
It will also be important to understand what changes will come about in natural
systems as they become part of the matrix of land uses in the urban landscape. Natural
areas are being converted to urban development faster than urban population growth, and
the sprawl is engulfing the habitat of many species. Although urban and residential areas
cover a small percentage of the land area, they often overlap with hotspots of biodiversity
and critical habitat (Hansen et al. 2005). As a result, many species are exposed to a novel
ecosystem that has the potential to unravel the integrity of ecological communities and
alter regional biodiversity.
Cities offer a unique opportunity to study basic questions in ecology. Many cities
are at least partly planned and different designs provide opportunities for natural
experiments in environmental change. Pickett et al. (2011) recently reviewed the major
environmental changes introduced by urban development to natural areas. Most obvious
is the removal of vegetation in forested biomes and the addition of canopy cover in
prairie and desert biomes. Structural modification of habitat can greatly affect local
2 climate and the interface between urban climate and organisms is the focus of this
dissertation.
The biophysical properties of different land cover types within a city create the
complex urban climate. At the landscape level, the net effect of the urban matrix may be
warmer temperatures in the city than surrounding natural areas. The urban heat island
effect exists for some cities, like Baltimore, in temperate forested regions of the United
States where canopy cover is replaced by impervious surfaces. However, cities in arid
regions, like Phoenix, may actually be cooler than surrounding natural areas from
increased irrigation and canopy cover. The patchy mosaic of fine-scale heterogeneity in
land cover types may be more important in determining microclimate within a city than
the city-wide elevation in temperature (Peterson 2003). Organisms also interact with the
thermal environment at finer spatial scales and temperature changes at the landscape or
patch scale can have profound impacts on organisms.
The phenology of many organisms is temperature sensitive and may shift along an
urban thermal gradient. Plants have already been observed to do so (Zhang et al. 2004).
The timing of breeding in Florida Scrub-jays (Aphelocoma coerolescens) advanced in
cities compared to natural areas, but the shift was attributed to food resources, not
temperatures (Schoech and Bowman 2003). The role of urban climate in the phenology
of other taxa remains largely unexplored.
Fitness of an organism can also be temperature sensitive, and only recently have
the effects of urban climate on population dynamics been studied (Meineke et al. 2012).
Physiological limitations may increase the risk of heat stress for some species in urban
habitat, and reproduction can also be affected by microclimate. Eggs laid by long-tailed
3 skinks (Euthopis longicaudata) in concrete walls in urban areas were incubated at
warmer temperatures and produced larger hatchlings than eggs laid under rocks in natural
areas (Huang and Pike 2011). The fitness benefit for lizards nesting in urban habitat was
lost after urban temperatures increased disproportionately from climate change and
became too warm for egg development.
Measures of fitness in urban habitat are necessary for establishing habitat quality.
A diverse community of native species can inhabit cities and suburbs, yet presence does
not signify persistence. Measurements related to reproduction and survival may be more
reliable indicators (Aldridge and Boyce 2008, Johnson 2007) for helping to identify
source and sink populations. The influence of urban development on productivity in
birds does not show a consistent pattern across studies (Chamberlain et al. 2009), despite
there being plenty of anthropogenic sources for lower survival and reproduction. House
sparrows (Passer domesticus) at more urbanized sites were smaller and in poorer
condition (Liker et al. 2008), suggesting that urban habitat was lower quality despite
higher bird abundance. In contrast, mockingbird survival and productivity increased in
urban sites, indicating a source population (Stracey and Robinson 2012).
Even seemingly advantageous resources in urban habitat may undermine nest
success. Organisms may have evolved to respond to specific cues that increase fitness in
natural habitat, but those cues can become decoupled from fitness in urban habitat. For
example, earlier nesters are often the more successful breeders and produce more young
than nests started later in the breeding season. Earlier leaf out by Lonicera sp. at urban
sites initiated earlier nesting by higher quality Northern Cardinals (Cardinalis
cardinalis), yet those nests had lower estimated nest survival rates (Leston and Rodewald
4 2006, Rodewald et al. 2011). Such ecological traps could also result from urban climate
influences on nest phenology and nest success.
Habitat modifications from urban development are localized and not independent
of regional changes occurring simultaneously. An emerging area of research is the
synergistic effects of land use change and climate change on distributions and population
dynamics (Mantyka-Pringle et al. 2012). The same predictions of species distributions
under climate change scenarios can be predicted by land use change, potentially masking
the signal of one or the other (Clavero et al. 2011). The rapid rate of land use change
may have more of an impact than climate change effects for some sensitive species
(Wilson et al. 2013). Land use change may also provide opportunities to buffer
populations against negative consequences of climate change (Clausen et al. 2013).
Many cities will be affected by climate change without a current mitigation plan in place
(Stone et al. 2012, Tyler and Moench 2012) and improving resiliency should be a
priority.
The objectives of this project were to better understand the relative importance of
temperature and land cover on nest survival, the function of thermal refuges for nest sites,
and the influence of urban climatic variability on phenology. The three studies touch on
global change processes with the intention of improving urban habitat resiliency while
addressing questions relevant to ecology. Monitoring of temperatures, trees,
invertebrates, and breeding birds occurred in a riparian corridor along an urban to rural
gradient. Riparian habitat provides a disproportionate level of ecosystem services in arid
biomes and urban development is the most common new land use in US catchments
5 (Jones et al. 2010). Birds received most of the attention in these studies as they are
readily observable indicators of biodiversity and ecosystem health.
The impacts of these studies extend well beyond conservation of native species.
Maintaining biodiversity in and around cities may help to preserve the ecosystem
functions on which people depend. Examples include the pollination of landscaped
plants and urban gardens, sewage treatment, rainwater drainage, noise reduction, air
filtration, energy savings, and recreational opportunities (Alberti 2005, Bolund and
Hunhammer 1999, Taha 1996). Mental health and well-being are also at risk from a
decline in native species richness. Cognitive abilities and childhood learning may
improve in the presence of trees and other vegetation (Wells 2000). Experiencing nature
or biodiversity on a frequent basis may strengthen family and neighborhood relationships
(Dustin et al. 2010) or improve mental health (Dean et al. 2012). Perhaps most
importantly, childhood experiences in nature, even in ways as simple as exploring a
backyard, influence adult attitudes towards environmental issues (Wells and Lekies
2006). The significance of urban biodiversity may be lost to future generations if it is not
conserved now.
Fortunately, cities have the potential to incorporate thoughtful ecological design
(Niemala et al. 1999), including microclimate. State and municipal governments are the
most effective at implementing successful conservation strategies, and some cities are
planning for biodiversity (Connery 2009, de Oliveira et al. 2011). Unlike natural systems
in the United States, much of the land surface in urban habitat is privately owned and
managed. Backyards and residential gardens offer substantial resources for organisms
(Davies et al. 2009, Doody et al. 2010, French et al. 2005, Larson et al. 2009) and they
6 may be as important for urban biodiversity as public open spaces. Using those resources
to the fullest advantage will require coordinated efforts (Hostetler and Drake 2009,
Lerman et al. 2012). Citizen participation in urban ecology research is growing and
provides an opportunity to educate the public about conservation (Cooper et al. 2007,
Ryder et al. 2010). Close partnerships between urban dwellers and urban ecologists will
hopefully increase the pace of learning and achieve biodiversity conservation.
7 LITERATURE CITED
Alberti, M. 2005. The effects of urban patterns on ecosystem function. International
Regional Science Review 28: 168–192.
Aldridge, C.L. and Boyce, M.S. 2008. Accounting for fitness: combining survival and
selection when assessing wildlife-habitat relationships. Israel Journal of Ecology &
Evolution 54: 389–419.
Bolund, P. and Hunhammar, S. 1999. Ecosystem services in urban areas. Ecological
Economics 29: 293–301.
Chamberlain, D.E., Cannon, A.R., Toms, M.P., Leech, D.I., Hatchwell, B.J., Gaston, K.J.
2009. Avian productivity in urban landscapes: a review and meta-analysis. Ibis 151:
1–18.
Clausen, K.K., Stjernholm, M., Clausen, P., 2013. Grazing management can counteract
the impacts of climate change-induced sea level rise on salt marsh-dependent
waterbirds. Journal of Applied Ecology 50: 528–537.
Clavero, M., Villero, D., Brotons, L., 2011. Climate change or land use dynamics: do we
know what climate change indicators indicate? PLoS ONE 6.
Connery, K. 2009. Biodiversity and urban design: seeking an integrated solution. Journal
of Green Building 4: 23–38.
Cooper, C.B., Dickinson, J., Phillips, T., Bonney, R. 2007. Citizen science as a tool for
conservation in residential ecosystems. Ecology and Society 12.
Davies, Z.G., Fuller, R.A., Loram, A., Irvine, K.N., Sims, V., Gaston, K.J. 2009. A
national scale inventory of resource provision for biodiversity within domestic
gardens. Biological Conservation 142: 761–771.
8 De Oliveira, J.A.P., Balaban, O., Doll, C.N.H., Moreno-Penaranda, R., Gasparatos, A.,
Iossifova, D., Suwa, A. 2011. Cities and biodiversity: Perspectives and governance
challenges for implementing the convention on biological diversity (CBD) at the city
level. Biological Conservation 144: 1302–1313.
Dean, J., van Dooren, K., Weinstein, P. 2011. Does biodiversity improve mental health in
urban settings? Medical Hypotheses 76: 877–880.
Doody, B.J., Sullivan, J.J., Meurk, C.D., Stewart, G.H., Perkins, H.C. 2010. Urban
realities: the contribution of residential gardens to the conservation of urban forest
remnants. Biodiversity and Conservation 19: 1385–1400.
Dustin, D.L., Bricker, K.S., Schwab, K.A. 2010. People and Nature: Toward an
Ecological Model of Health Promotion. Leisure Sciences 32: 3–14.
French, K., Major, R., Hely, K. 2005. Use of native and exotic garden plants by suburban
nectarivorous birds. Biological Conservation 121: 545–559.
Hansen, A.J., Knight, R.L., Marzluff, J.M., Powell, S., Brown, K., Gude, P.H., Jones, A.
2005. Effects of exurban development on biodiversity: Patterns, mechanisms, and
research needs. Ecological Applications 15: 1893–1905.
Hostetler, M. and Drake, D. 2009. Conservation subdivisions: A wildlife perspective.
Landscape and Urban Planning 90: 95–101.
Huang, W.-S. and Pike, D.A. 2011. Climate change impacts on fitness depend on nesting
habitat in lizards. Functional Ecology 25: 1125–1136.
Johnson, M.D. 2007. Measuring habitat quality: A review. Condor 109: 489–504.
Jones, K.B., Slonecker, T.E., Nash, M.S., Neale, A.C., Wade, T.G., Hamann, S. 2010.
Riparian habitat changes across the continental United States (1972–2003) and
9 potential implications for sustaining ecosystem services. Landscape Ecology 25:
1261-1275.
Larson, K.L., Casagrande, D., Harlan, S.L., Yabiku, S.T. 2009. Residents’ yard choices
and rationales in a desert city: social priorities, ecological impacts, and decision
tradeoffs. Environmental Management 44: 921–937.
Lerman, S.B., Turner, V.K., Bang, C. 2012. Homeowner associations as a vehicle for
promoting native urban biodiversity. Ecology and Society 17.
Leston, L.F.V. and Rodewald, A.D. 2006. Are urban forests ecological traps for
understory birds? An examination using Northern cardinals. Biological Conservation
131: 566–574.
Liker, A., Papp, Z., Bokony, V., Lendvai, A.Z. 2008. Lean birds in the city: body size
and condition of house sparrows along the urbanization gradient. Journal of Animal
Ecology 77: 789–795.
Mantyka-Pringle, C.S., Martin, T.G., Rhodes, J.R. 2012. Interactions between climate
and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global
Change Biology 18: 1239–1252.
Meineke, E.K., Dunn, R.R., Sexton, J.O., Frank, S.D. 2013. Urban warming drives insect
pest abundance on street trees. PLoS ONE 8: e59687.
Niemela, J. 1999. Ecology and urban planning. Biodiversity and Conservation 8: 119–
131.
Peterson, T. 2003. Assessment of urban versus rural in situ surface temperatures in the
contiguous United States: No difference found. Journal of Climate 16: 2941–2959.
10 Pickett, S.T.A., Cadenasso, M.L., Grove, J.M., Boone, C.G., Groffman, P.M., Irwin, E.,
Kaushal, S.S., Marshall, V., McGrath, B.P., Nilon, C.H., Pouyat, R.V., Szlavecz, K.,
Troy, A., Warren, P. 2011. Urban ecological systems: Scientific foundations and a
decade of progress. Journal of Environmental Management 92: 331–362.
Rodewald, A.D., Shustack, D.P., Jones, T.M., 2011. Dynamic selective environments and
evolutionary traps in human-dominated landscapes. Ecology 92: 1781–1788.
Ryder, T.B., Reitsma, R., Evans, B., Marra, P.P. 2010. Quantifying avian nest survival
along an urbanization gradient using citizen- and scientist-generated data. Ecological
Applications 20: 419–426.
Schoech, S. and Bowman, R. 2003. Does differential access to protein influence
differences in timing of breeding of Florida scrub-jays (Aphelocoma coerulescens) in
suburban and wildland habitats? Auk 120: 1114–1127.
Stone, B., Vargo, J., Habeeb, D. 2012. Managing climate change in cities: Will climate
action plans work? Landscape and Urban Planning 107: 263–271.
Stracey, C.M. and Robinson, S.K. 2012. Are urban habitats ecological traps for a native
songbird? Season-long productivity, apparent survival, and site fidelity in urban and
rural habitats. Journal of Avian Biology 43: 50–60.
Taha, H. 1996. Modeling impacts of increased urban vegetation on ozone air quality in
the South Coast Air Basin. Atmospheric Environment 30: 3423–3430.
Tyler, S. and Moench, M. 2012. A framework for urban climate resilience. Climate and
Development 4: 311–326.
Wells, N. 2000. At home with nature - Effects of “greenness’s on children’s cognitive
functioning. Environment and Behavior 32: 775–795.
11 Wells, N.M. and Lekies, K.S. 2006. Nature and the life course: pathways from childhood
nature experiences to adult environmentalism. Children, Youth and Environments
16: 1-25.
Wilson, J.N., Bekessy, S., Parris, K.M., Gordon, A., Heard, G.W., Wintle, B.A. 2013.
Impacts of climate change and urban development on the spotted marsh frog
(Limnodynastes tasmaniensis). Austral Ecology 38: 11–22.
Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Schneider, A. 2004. The
footprint of urban climates on vegetation phenology. Geographical Research Letters
31.
12 SYNERGISTIC EFFECTS OF SPRING TEMPERATURES AND LAND COVER ON
NEST SURVIVAL OF URBAN BIRDS
MILES E. BECKER AND PETER J. WEISBERG
ABSTRACT
Climate change has the potential to influence population dynamics through a response in
vital rates to temperature. Avian nest fate may be vulnerable to extreme weather events
or directional shifts in spring temperatures during the breeding season. Nest fate varies
across spatially heterogeneous habitat as well, and changing land uses may introduce
additional stressors that contribute to reproductive outcome. We studied the nest fate of
three sympatric species breeding in urban habitat in an arid region forecasted to become
warmer. Nests of American Robin (Turdus migratorius), Mourning Dove (Zenaida
macroura), and Black-headed Grosbeak (Pheucticus melanocephalus) were monitored
over four years at seven sites on an urban to rural gradient. Nest fate in relation to
temperatures at the time of egg laying and after eggs hatched was analyzed with logistic
exposure models. Microhabitat measurements at the nest included nest height and the
amount of canopy cover and impervious surfaces at three spatial scales of 20m, 100m,
and 500m radii from the nest. Models with temperature deviation after hatching fit the
data best when considering a quadratic relationship with temperature, nest stage, finescale canopy cover, and pedestrian traffic. Nest survival increased mainly at warmer
temperature anomalies during the nestling stage. The nests of all three species were more
likely to survive at locations with more canopy cover in a 20m radius and at sites with
more pedestrian traffic. Warming spring temperatures may be beneficial for some
breeding birds and increasing cover of native riparian canopy at fine scales in urban
reserves may enhance habitat quality. For understanding vital rate and population
responses to climate change, it will be important to simultaneously consider land-use
influences on local habitat.
13 14 INTRODUCTION
The impacts of global climate change on avian populations are widely recognized
(reviewed in Møller et al. 2010), yet the underlying mechanisms are not well established.
Several studies have demonstrated that the effects of changing temperatures or
precipitation on the distribution or population dynamics of a species are often indirect.
Examples include the contraction or expansion of current bioclimatic envelopes that can
limit habitat availability (Virkkala et al. 2008) or help establish novel communities (Lurgi
et al. 2012) where encounters with novel predators, parasites, or diseases could increase.
Phenological shifts in the timing of breeding, or the absence of a shift, in response to
temperature-related cues may lead to a temporal asynchrony between peak prey
availability and peak nutrient demand by nestlings (the mismatch hypothesis: Visser et al.
1998). Other trophic interactions, such as predation rates, may be influenced by the
amount of overlap in the activity of breeding birds and their predators (Both et al. 2009).
Climate change also has the potential to negatively impact vital rates through
direct abiotic effects. Heat waves are becoming more common and may increase
mortality rates of adult birds in some regions (McKechnie and Wolf 2010). In addition to
extreme stochastic events, slight increases in background temperatures could influence
the reproductive success of birds breeding in temperate and high latitude areas. Egg
viability is sensitive to temperatures above and below those needed to initiate embryonic
development – physiological zero temperature (PZT) – and warmer ambient temperatures
during incubation may decrease hatching success (Aldredge et al. 2012). Nestlings and
chicks with low thermoregulatory ability are also vulnerable to heat stress. For example,
15 the proportion of fall harvest rates of juvenile sharptail grouse were negatively correlated
with the number of days above chick heat stress tolerance during the breeding season
(Flanders-Wanner et al. 2004).
Alternatively, warming temperatures during the nesting period could benefit
reproductive productivity. Temperature-related nest failure is more often associated with
cold and wet weather and may be alleviated by regional warming. Hypothermia is a
serious hazard for underdeveloped young. Oxidative stress in precocial chicks (Gallus
gallus) maintained at low temperatures (20 °C) for 12 hours caused damage to heart and
brain tissue (Mujahid and Furuse 2009). Increased nest survival at warmer temperatures
during the breeding season has been documented in cavity nesters (Hollenbeck et al.
2011, Newlon and Saab 2011) and open-cup nesting species (Skagen and Adams 2012,
Wright et al. 2009). Cases of the opposite response, increased nest survival at cooler
temperatures, are reported (Dreitz et al. 2012) but rare, even for arid regions with extreme
heat during the breeding season.
Spring warming trends may therefore detract from or improve successful nesting
attempts. It is also unclear if nest failure is more likely at extreme temperatures or simply
at slight deviations from baseline temperatures. The stage of the nesting cycle,
incubation or nestling, that is most sensitive to temperature variation needs elucidation as
well. Wood Duck (Aix sponsa) eggs experimentally maintained at 0.9 – 2.3 °C cooler
temperatures incubation produced females that were less likely to fledge, survive their
first winter, and reproduce (Hepp and Kennamer 2012). In contrast, the daily minimum
temperatures for the first 15 days after hatching in Great Tit (Parus major) nests
explained most of the variation in post-fledging survival (Greno et al. 2008). Seasonal
16 variation in temperatures during the separate stages of the nest cycle may have
independent effects on nest success. The relative sensitivity of nest survival to spring
temperatures is poorly understood, especially for passerines breeding in temperate
regions, and could explain population declines in addition to other climate change
induced factors.
Any temperature-related influences on nest survival are likely to be mediated by
habitat. Different land covers have different biophysical effects on climate through a
change in albedo, surface roughness, heat capacity, or water retention (Vargo et al. 2013).
Concurrent with climate change, the ranges of many bird populations are undergoing
rapid land use changes and land cover modification, with implications for local
temperatures. The fastest growing global land use is urban development (Seto et al.
2012) that typically modifies the thermal environment in similar ways to forecasted
regional temperatures for many natural areas, such as milder winters and warmer
summers (Grimmond and Oke 1999, Oke 1988, Pickett et al. 2011). Thermodynamics in
urban areas may influence breeding birds in similar ways as climate change would in less
disturbed habitat, although the signal may be difficult to detect.
Many other anthropogenic stressors exist in urban areas (Chamberlain et al. 2009)
that may mask temperature effects on nest survival. Air and noise pollution, nestling
food quality, and nest predation risk can change with urban development (Halfwerk et al.
2011, Peach et al. 2008, Robb et al. 2008, Ryder et al. 2010, Thorington and Bowman
2003). Dramatic and immediate land cover disturbances may be more important for nest
survival than local temperature variation or long-term climate change, as it was for less
developed systems (Clavero et al. 2011, Cox et al. 2013). Identifying the relative
17 importance of climate change and land use change, synergistic effects, and interactions is
necessary for mitigating the impact of global change on native species (Mantyka-Pringle
et al. 2012).
We studied the influence of variation in spring temperatures during incubation
and the nestling period on nest survival of three sympatric songbird species in northern
Nevada, USA. Arid lands in the Southwest and Intermountain West regions of North
America, including the Great Basin, are forecasted to become warmer (Diffenbaugh et al.
2008). The study area included urban habitat with substantial land cover heterogeneity
within breeding territories. Our primary objectives were to determine 1) how much
variation in nest survival was explained by temperature deviation from the norm,
compared to more extreme temperatures above and below physiological tolerances of
eggs and nestlings; 2) which stage of the nest cycle was most sensitive to temperature
variation; and 3) the relative importance of habitat at the nest compared to temperatures
during nesting. We expected more extreme heat or cold to influence nest survival more
than slight deviations from background temperatures. We predicted that young nestlings
would be more vulnerable than recently laid eggs to temperature stress for two reasons.
First, temperatures during the incubation stage are closer in time to the favorable weather
conditions used as a cue by a female to lay eggs. Second, food demand by nestlings puts
more pressure on adults to leave the nest and forage, thereby leaving young chicks
exposed to the weather. We expected that in a disturbed urban ecosystem, land cover and
anthropogenic effects would be more important for nest survival than temperatures.
METHODS
18 Study Area and Species
Our study system was a series of municipal parks on a 15.25 km transect through
the cities of Reno and Sparks in northern Nevada (Figure 1). The city boundaries have
expanded outward rapidly in the past 40 years, establishing residential areas, suburbs, and
industrial parks in a complex patchwork of land uses. Areas of natural vegetation,
primarily sagebrush communities, and farmland remain where the boundaries of
developed land parcels abruptly end. We selected seven public parks ranging in size
from 3 ha to 18 ha that all contained at least some native riparian vegetation, were easy to
access, and represented the composite land covers available in the urban matrix and
reserves.
The study area is on the border of the Great Basin and eastern Sierra Nevada
where there is strong inter-annual, seasonal, and intra-seasonal temperature variation.
Average winter temperatures are 7° C and summers reach highs of 33° C. The region is
fairly arid and most precipitation falls as snow during winter. Temperatures during the
breeding season in spring and summer can vary greatly over a short period of time
(Figure 2).
The three study species, American Robin (Turdus migratorius, hereafter robin),
Black-headed Grosbeak (Pheucticus melanocephalus, grosbeak), and Mourning Dove
(Zenaida macroura, dove), differ in length of the nesting season and nest habitat
preferences. Doves and robins initiate nesting in early spring when temperatures are
often cold and unpredictable and continue to nest through summer. Doves and robins are
also synanthropic habitat generalists and construct nests in a wide range of substrates.
19 Grosbeaks arrive in the study area the very end of April and begin breeding in mid-May
when temperatures are usually warmer. Grosbeaks typically nest in denser vegetation
close to the river and they are less likely to be found in areas with high levels of human
activity.
Nest Monitoring
We monitored 159 robin nests, 163 dove nests, and 49 grosbeak nests from 2009
to 2012. We began nest searching in late March or early April and located nests using
behavioral cues of the adults or by visually scanning vegetation. All nests used in the
analysis were lower than 8m because limited visibility of higher nests made it difficult to
accurately assess nest age and fate. A mirror pole was used to examine nest contents
unless parents were observed feeding chicks or fledges. Nests were visited every three to
five days until the nest failed or nestlings fledged. Visits between study sites were
systematically rotated to ensure consistent search effort across all parks.
Females lay one egg a day and the day the first egg was laid could be estimated
with a high level of accuracy for nests observed in the middle of the laying sequence. For
all other nests, we estimated clutch initiation by counting back from hatch day or by
aging nestlings. Nestlings of uncertain hatch date were aged using reference photographs
taken of nests with the hatch day observed. A single observer aged all nests.
Nest fate was determined from the nest contents at consecutive visits to the nest.
Nests empty more than four days before expected fledge date were recorded as failed.
Nests with live nestlings observed until at least within two days of the expected fledge
date were designated successful if there was also supporting evidence of fledging from
20 auditory or visual observations, such as adults bringing food to fledges near the nest tree.
Alternative sources of nest failure, such as abandonment or depredation, could not be
distinguished for most nests. Predators may consume eggs or chicks after parents
abandon a nest and before the next visit by an observer, making it difficult to know the
primary source of nest failure. Since temperatures can affect nestling physiology and
parental behavior in ways that influence rates of both abandonment and nest depredation,
it was not possible to separate out sources of nest failure from the protocol we used.
One potential source of nest failure that may have differed between the study
species was nest parasitism by Brown-headed Cowbirds (Molothrus ater) in grosbeak
nests. Cowbirds are one of the more common species in the study area during the
breeding season (Lynn et al. 1998) and they may be an additional influence on grosbeak
nest survival. Cowbird females deposit their eggs in the host nest during egg laying by
the host, and parasitism is sometimes associated with increased rates of nest failure (Cox
et al. 2012, Hannon et al. 2009). We recorded the presence (cow egg) and number of
cowbird eggs (cow num) and nestlings in grosbeak nests.
Temperatures
Daily temperatures in the study area are recorded continuously by a National
Weather Station at the Reno Tahoe International Airport located in close proximity to the
study sites (Figure 1). We used five measures of temperature variability. The first was
intended to detect subtle changes in background levels of ambient temperatures. Average
daily temperature deviation from the 20-yr average (Tdev) was calculated for every day
of the nesting cycle for all nests with known age. Tdev avoids confounding day of the
21 season with temperatures, because actual temperatures generally increase as the breeding
season progresses. Tdev also provides a benchmark for biologically significant
temperatures above or below the normal range of temperatures nesting birds may have
experienced or adapted to in the past twenty years. We then calculated average Tdev for
a moving window in the five days after clutch initiation (Cdev) and five days after
hatching (Hdev) to isolate the relative importance of those two stages. A five day
window was used because complete incubation and full nest attentiveness starts after the
final egg is laid (Morton and Pereyra 2010, Wang and Weathers 2009) and songbird
nestlings are least capable of thermoregulation the first few days after hatching (Pereyra
and Morton 2001).
The other two temperature metrics were intended to capture heat events beyond
the physiological tolerances of eggs or nestlings. Physiological zero temperatures range
from 24 – 27 °C and we used a conservative estimate of the upper limit to calculate the
number of days with Tmax above 27 °C during the five days after clutch initiation for
each nest (pzt). The temperature threshold for nestling survival is less specific, but
reportedly 11 – 20 °C may be required for mortality or tissue damage (Mujahid and
Furuse 2009, Rippon et al. 2011). We used an intermediate threshold value of 15 °C that
may correspond to negative consequences for nestlings in this study. The number of days
with Tmax below 15 °C in the first five days after eggs hatched was also calculated for
each nest that survived to hatching (u15).
Habitat
22 While we were primarily interested in the influence of temperature on nest
success, nest site habitat could also mediate predation risk. Nest height was a significant
factor for nest survival of Northern Cardinals (Cardinalis cardinalis; Smith-Castro and
Rodewald 2010), silvereyes (Zosterops lateralis) and blackbirds (Turdus merula; van
Heezik et al. 2008) breeding in urban habitat. Increased vegetation density surrounding
the nest may also decrease detection by predators (Chalfoun and Martin 2009). Nest
habitat variables considered in the analysis were nest height (nest ht), percentage of
canopy cover within a 20m radius (canopy20), and percentages of impervious surface and
all vegetation within a 100m radius (impev100, plants100) and 500m radius (imperv500,
plants500). We measured nest height with a clinometer to the nearest meter. Quantities
of land cover types within specified radii were calculated in ArcGIS (ArcMap 10, ESRI
Software, Redlands, CA, USA) using layers provided by the City of Reno (AMEC 2012).
The five land cover types were classified using a geographic object-based image analysis
based on 1m resolution orthophotographs captured in summer 2010 by the National
Agriculture Imagery Program that yielded an overall accuracy of 94%.
Human and Predator Activity
The seven study sites covered a gradient of urban development that likely
influenced predator populations and disturbance from recreational park use. Some of the
more active predators in urban areas are cats (Balogh et al. 2011) and corvids (Marzluff
et al. 2007). Pedestrians also have the potential to disturb birds in urban areas
(Schlesinger et al. 2008). Too few cats were detected to reliably estimate densities, so we
used only corvid data for a predator activity index at a site. We estimated relative site
23 level densities of corvids, and pedestrians (corvid, peds) by distance sampling (Buckland
et al. 2001, Gottschalk and Huettmann 2011) along transects through each park in the
2011 breeding season. Predator and pedestrian densities were only estimated in one year
(2011) even though nesting data were collected over four years (2009-2012). However,
resources that attract neighborhood predators and people to a park are fairly constant
from year to year.
Line transects followed a foot or bicycle path through the entire length of each
site and generally ran parallel to the river. The perpendicular distance from an observer
on the line to a detected bird or predator was measured with a laser rangefinder (Sport
450, Bushnell, Overland Park, Missouri, USA). Transects were started between 0630 and
1000 and sites were assigned to one of three groups based on geographic proximity. The
sampling order within a group was rotated at every visit to avoid a temporal bias. Within
a round, all groups were sampled over 3 days and repeated 8 times over the season every
5-8 days for a constant observer effort between sites. The direction of travel on transects
from start to finish alternated between East and West on consecutive visits.
ANALYSIS
Predator and Pedestrian density
Site level densities of potential corvid predators were estimated in Program
DISTANCE (version 6.0 Release 2, Research Unit for Wildlife Population Assessment)
that avoids biased estimates from decreasing detection probabilities at farther distances.
Total density estimates were divided by the number of visits to the same site.
24 Observations were treated as clusters and distances were considered to be perpendicular.
Data were stratified by site and estimated at the site level using the negative exponential
key function and cosine series expansion. The functions were chosen because they best
represented the distribution of the data and series expansions had the lowest AIC scores
when compared to models with other combinations of functions.
Pedestrian traffic was estimated with a belt transect estimate using a fixed
distance of 100m on either side of the footpath. Belt transects were used for pedestrian
estimates because we were able to observe everyone traveling on designated paths in
relatively open habitat with a detection probability of approximately 100%. We
estimated number of pedestrians per hour in Program DISTANCE using a uniform key
function and cosine series expansion.
Nest Success
Nest survival was analyzed in Program MARK (White and Burnham 1999) for a
series of logistic exposure models built with different combinations of individual
covariates. Given that nests were clustered in time (years) and space (sites), we first
examined site, year, and species level effects by placing nests into groups nested by those
three factors. Species was the only group that had an effect on nest survival and data
were pooled across years and sites. The two parameter columns for species groups in the
design matrix were included in models built with the individual covariates for nest
temperature (Tdev, Cdev, Hdev, pzt, u15) and habitat (nest ht, canopy20, imperv100,
plant100, imperv500, plant500, corvid, peds). We standardized all covariates prior to the
25 survival analysis and employed the logit link function for maximum likelihood
estimation.
Tdev was a time varying covariate where every day of the nest cycle in the design
matrix corresponded to a unique daily value for each nest in the input file. Temperatures
during egg laying (Cdev, pzt) only contributed to variance in daily nest survival during
incubation in the models, while temperatures in the first five days after hatching (Hdev,
u15) were designed to influence only nestling survival. Two additional time varying
covariates were built into models using the design matrix. The day of clutch initiation for
a nest relative to the start of that year’s breeding season (season) was included in a single
column parameter and increased for every consecutive day in the nesting cycle to
represent the progression of dates over time. All days over the entire nest cycle were
assigned to a nest stage of egg laying, incubation, or nestlings (stage) for a time varying
trend in the design matrix. Habitat at a nest was unchanged for the duration of that nest
cycle and those variables were consistent over time.
The relative fit of all models in the candidate set was assessed with an
Information Theoretic approach using Akaike’s Information Criterion adjusted for small
sample sizes. Higher performing models were given support by smaller ΔAICc and
larger AICc weights. Temperature and habitat covariates were first modeled
independently and the variables in better performing models were selected for inclusion
in additive models with other variables. We also modeled a quadratic term and species
interaction with individual covariates prior to selecting the variable that best represented
variance in daily survival rates. Important variables were included in the same number of
26 candidate models to provide an unbiased AICc weight. Only a summary of the top
models are presented in the results for simplicity.
Of 371 nests observed, 54 were of an unknown age and an additional 74 failed
during incubation. Prior to z-transforming the data, missing values for nests with an
unknown age were assigned the population average variables that were time dependent:
Tdev, Cdev, Hdev, pzt, and u15. Nests that failed before eggs hatched could not have
been influenced by Hdev or u15, and those empty cells were assigned the population
average as well. Average values become zero after z-transformation and so did not
influence estimates of nest survival.
Daily survival estimates from individual parameters of the top model were back
transformed from the logit scale. Estimates are often averaged among the top competing
models within 4 ΔAICc to account for model uncertainty, but the top model in our
candidate set differed by more than 4 ΔAICc from the next best model. Cumulative daily
survival, or nest survival probability, was calculated by raising daily survival rate to the
number of days in the nesting cycle of an individual species. We calculated 95%
confidence intervals for beta estimates by bootstrapping back transformed standard errors
at selected user-specified values.
RESULTS
All values are mean ± SD unless otherwise indicated.
Temperatures
27 Average daily temperatures increased from 6.9 °C (± 3.9 °C) in March to 19.6 °C
(± 4.5 °C) in June over the entire four year study period. Five-day windows of
temperatures during incubation and hatching were more often below the 20-yr average,
indicating the study period was colder than normal. Within the study period, 2009 and
2012 were warmer springs (Figure 2). Average daily temperatures between the first and
second quartiles of clutch initiation were colder for doves and robins (11.7 °C and 11.8
°C) than for grosbeaks (15.0 °C), which is not surprising since the first quartile of
grosbeak nests was started 21 to 37 days later than robins and doves, respectively. Most
nests were initiated within ± 5 °C of the 20-yr norm across all years and appeared to be
evenly distributed around ± 0 °C of the norm (Figure 3).
Habitat
Most nests we observed were constructed at mid- to low-level heights relative to
the upper canopy available in mature trees in the study area (11.9m ± 7m). Doves and
robins built significantly higher nests (4.2m ± 1.6m and 4.0m ± 1.6m) than grosbeaks
(2.9m ± 1.5m; F2,370 = 11.941, P < 0.001). Canopy cover within 20m of a nest was also
significantly greater for dove nests (31.1% ± 16.8%) and robin nests (28.1% ± 18.1%)
than for grosbeak nests (22.2% ± 16.5%; F2,370 = 5.050, P = 0.007). Across all species,
canopy and understory vegetation was 53.0% ± 13.4% and impervious surfaces were
7.4% ± 19.8% of the land cover within 100m of nests.
Pedestrians, Corvids, and Cowbirds
28 The estimated number of pedestrians walking through a site every hour ranged
from none to 136 (31.4 ± 30.3). Estimated corvid density at sites ranged from 2.7 to 56.4
birds per hectare (10.5 ± 11.3). Cowbird parasitism could be reliably confirmed or
rejected in only 36 of the 49 grosbeak nests. Of those nests with known presence or
absence of cowbird eggs and chicks, 75.7% were parasitized. The average number of
eggs in parasitized nests was 1.7 (± 0.9) and did not differ significantly between failed
and fledged nests (F1,36 = 0.242, P = 0.626; Figure 4A). The Cdev during egg laying did
not differ significantly between nests with one, two, three, or no cowbird eggs (F3,35 =
0.190, P = 0.902; Figure 4B).
Nest Success
Several variables were important for variation in daily nest survival. Both dove
and robin nests were more likely to fail during the nestling stage (doves: β = -0.932 ±
0.259 SE; grosbeaks: β = -1.049 ± 0.408 SE). Temperature during the first five days after
eggs hatched (Hdev) was the most important temperature variable considered when fit
with a quadratic term and included with species and nest stage (Table 1). Estimates of
nestling survival probabilities from hatching to fledging were lowest at temperatures -2
°C from the 20-yr norm (Figure 5). Nestling survival increased by 12% for every +1°C
above the 20-yr norm and also increased at temperature anomalies below -2 °C.
Substituting Hdev with slight changes in deviation from normal temperatures on any
given day in the nesting period (Tdev) or during egg laying (Cdev) did not improve the
model (ΔAICc = 10.89, wi = 0.004; ΔAICc = 9.35, wi = 0.009). The models with the two
variables for more severe temperatures, daily maximum temperatures over 27 °C (pzt) or
29 below 15 °C (u15), did not fit the data very well (ΔAICc = 22.92, wi = 0; ΔAICc = 22.36,
wi = 0).
For the habitat variables considered, canopy cover in a 20m radius best explained
variance in nest survival (Table 1). The cumulative probability of nest survival increased
by 3% for every increase of 10% canopy cover across all species (β = 0.159 ± 0.076 SE;
Figure 6A). Sites with heavier pedestrian traffic (peds) had a positive influence on nest
survival and 10 more people per hour translated into a 2% increase in cumulative nest
survival probability (β = 0.164 ± 0.085 SE; Figure 6B). Neither variation in corvid
predator densities (corvid) nor cowbird parasitism (cow egg, cow num) had much of an
influence on nest survival (corvid: ΔAICc = 21.88, wi = 0; cow egg: ΔAICc = 22.11, wi =
0; cow num: ΔAICc = 22.96, wi = 0).
DISCUSSION
Temperatures
Extreme weather has the potential to cause mass bird mortality (McKechnie and
Wolf 2010, Newton 2007), and such events are expected to increase in frequency in
association with climate change. Gradual directional changes in spring temperatures
could also affect nest survival, although we predicted extreme temperatures would be
more important. We did not find support for an effect of temperature extremes beyond
the physiological tolerances of embryo development or nestling thermoregulatory ability
on nest survival of robins, doves, or grosbeaks. Instead, a slight deviation of a few
30 degrees in ambient temperatures was enough to increase the probability of nest survival.
Excessive heat and cold may not have influenced nest survival in this study because days
with temperatures outside the observer-defined ‘optimal’ thermal window were rare
events in the study period. Facultative behavioral adjustments, such as a shift in nesting
phenology cued by favorable ambient temperatures, may also minimize the frequency of
extreme temperatures during the nesting cycle.
Extreme temperatures are difficult to define ecophysiologically. The temperature
range we considered to be within comfortable limits for eggs or nestlings, between 15 27 °C, may not necessarily be biologically meaningful. Eggs need to reach a critical
temperature threshold of 24 - 27 °C to initiate embryonic development (Webb 1987),
which is usually reached in altricial songbirds once the female begins complete
incubation after the penultimate egg is laid (Wang and Weathers 2009). Incubation
maintains egg temperature within the narrow range of 34-36 °C required for proper
development. A few days of warm weather during egg laying may prematurely initiate
embryonic development and expose eggs to daily minimum temperatures below the
optimal range, resulting in egg inviability (Beissinger et al. 2005). However, ambient air
temperatures differ from egg temperatures since egg cooling rate is partly dependent on
female nest attentiveness (Morton and Pereyra 2010, Wang and Weathers 2009). As
such, there is no universal ambient temperature for when eggs are at risk of
developmental arrest. Similarly, critical ambient temperature thresholds for passerine
nestling survival are not well established.
The sensitivity of nest survival for robins, doves, and grosbeaks to slight changes
in ambient air temperatures well within the range of background levels is consistent with
31 previous studies. Nests of the cavity nesting white-headed woodpecker in central Oregon
were more likely to fledge young at higher daily maximum temperatures (Hollenbeck et
al. 2011), as were Lewis’ woodpeckers in riparian woodlands of Idaho (Newlon and Saab
2011). Two ground nesting species, Lark Bunting (Calamospiza melanocorys) and wood
lark (Lullula arborea) also had higher nest survival at warmer average temperatures over
the breeding season (Skagen and Adams 2012, Wright et al. 2009). Only mountain
plovers (Charadrius montanus) breeding in eastern Colorado have been reported to
increase nest survival at cooler temperatures (Drietz et al. 2012). Weather during the
nesting cycle may also have only a weak effect or no effect on productivity (Bradbury et
al. 2003), suggesting that the magnitude and direction of sensitivity may be related to
species traits or habitat.
A few degrees of temperature deviation made a significant difference in nest
survival, but it is not clear why. Any negative consequences from cold or heat, such as
nestling hypothermia or heat stress, would have resulted in higher nest survival at normal
temperatures, the opposite of the quadratic trend we observed. Estimates of nest survival
increased mostly at warmer anomalies, especially for robins, and there may be a greater
benefit to nest survival at warmer actual temperatures with only a small benefit to nesting
at colder temperatures in some circumstances. Additional data on sources of nest failure
across a range of actual temperatures would be required to make an informed
interpretation of the observed response. Results from these data suggest that gradual
increasing spring temperatures associated with climate change may in fact benefit some
species although it is possible that at some upper critical threshold, temperatures could
become too warm. Increasing inter-annual temperature variability may also increase
32 productivity of some species. We were not able to observe the effects of extreme
temperatures (e.g. heat wave, late spring blizzards) on nest survival, and such weather
events may be detrimental to nest survival.
Nest Stage
Seasonal variation in temperatures during a single nest cycle may expose nest
stages to different temperatures that could influence nest fate. We expected that the first
five days after hatching would be a more vulnerable stage than egg laying. Nest survival
was most sensitive to temperature deviation in the nestling period immediately after
hatching when young nestlings are less capable of thermoregulation. Nestlings may be
more vulnerable than developing embryos to temperature variability because adults with
nestlings may face a trade-off between brooding young and leaving the nest to bring them
food. Eggs are self-nourishing and incubating adults may be more capable of buffering
eggs from suboptimal temperatures. Temperatures during egg laying could also be closer
in time to the preferred short-term temperatures used as cues by females to initiate
clutches (Wiebe and Gerstmar 2010).
Temperatures during egg laying may still have influenced overall reproductive
productivity even if we did not find evidence of an effect on survival. Deviation of egg
temperatures from PZT may affect egg inviability and hatching success (Olsen et al.
2008) that reduce brood size but do not necessarily affect nest fate. Brood reduction from
warmer temperatures can impact long-term population dynamics by reducing annual
productivity or offspring fitness, but this is not as severe of an outcome as complete nest
failure in response to temperatures at hatching.
33 Habitat
Urban habitat has been shown to influence nest survival and reproduction in
several ways. Air pollution can decrease nestling mass (Peach et al. 2008), traffic noise
can decrease clutch size and fledging rate (Halfwerk et al. 2011), food resources may be
of lower quality (Shawkey et al. 2004), and nest predator composition can change
(Rodewald and Kearns 2011). For all these reasons, we expected habitat to be important
for nest survival in this study, yet it was less important than temperature.
The relatively low importance of habitat variables may be due to the scale at
which the variables were measured. Ryder et al. (2010) found impervious surfaces and
canopy cover at a scale of 1000m were more important than at finer scales down to 100m
for nest survival of several species breeding within the matrix of Washington, D.C. In a
study of riparian habitat adjacent to urban areas, land use within 400m of the nest was the
most important factor in nest survival of Least Bell’s Vireo (Vireo bellii pusillus; Kus et
al. 2008). When only one scale was considered for another riparian species, microhabitat
within 5m of the nest of Yellow Warblers (Dendroica petechial) influenced nest survival
(Latif et al. 2011). The importance of habitat is probably dependent on the scale and
system studied.
Inconsistencies in definitions of urban habitat may also contribute to mixed
results on the influence of urban development on nest survival. Our study nests were
located in parks that, although were surrounded by an urban matrix of land use and land
cover, may have been less susceptible to disturbance than nests monitored in the matrix
itself. Continuous nest monitoring at locations on an urban to rural gradient in North
34 America occurs in urban reserves similar to ours (e.g. Marzluff et al. 2007), in larger
forested plots (e.g. Rodewald et al. 2011), and within backyards contained in the matrix
(Ryder et al. 2010). Inferring generalizations about the response of reproductive output
to urban development needs to consider the context in which the study sites are
described.
Urban habitat did influence nest survival in this study through variation in canopy
cover and pedestrian traffic. Canopy cover within 20m of the nest was positively
associated with a higher nest survival probability for all species. The benefit could be a
function of a number of factors, including the moderation of nest site temperatures.
Canopy cover, understory vegetation, and lawn generally cool air (Jenerette et al. 2007),
and some species may benefit from cooler nest temperatures at warmer times of the
season. Canopy cover can also increase nest survival by mitigating predation risk. Nests
in denser woody vegetation require more search effort from a predator to find than a nest
in an isolated tree or shrub (Chalfoun and Martin 2009). Some species may increase nest
survival with more protective cover at the nest (Goddard and Dawson 2009) and some
species select nest sites with more cover (Chalfoun and Martin 2007, Kolada et al. 2009).
The other important habitat factor that probably had more to do with predation
risk than temperature moderation was pedestrian traffic at a study site. Nest failure
increased in towns with more pedestrians at multiple sites across Europe (Jokimaki et al.
2005), which is different from the results of this study that sampled pedestrian activity at
much finer spatial scales. In general, predator abundance increases in urban areas even as
predation rates decrease, posing a predation paradox (Fischer et al. 2012, Rodewald et al.
2011). Urban areas may be a safe refuge for bird species able to tolerant human
35 disturbance because of a disproportionate negative effect of pedestrians on predators
(Møller 2012). The sites in our study with the most recreational users also had the least
amount of woody vegetation, which may normally serve as refugia for mammalian
mesopredators. The absence of suitable habitat and negative interactions with people
may lead to predator interference in the most urban sites.
Conclusions
Small deviations from baseline ambient temperatures during the nestling stage
were important for the nest survival of robins, doves, and grosbeaks, but it is not certain
if the response is ecologically significant enough to impact the population dynamics of
these or other species. Any true influence of temperature on population dynamics may be
masked by facultative adjustments to compensate for warming springs, like phenological
shifts in migration (Both et al. 2010, Møller et al. 2008) or breeding (Both et al. 2006), or
by selecting nest sites with more favorable microclimates.
The simultaneous modification of the local thermal and structural environment in
urban and residential areas will likely influence species differently than populations
responding to climate change in less developed habitats. The synergistic effects of land
use change and climate change are gaining recognition for their potential to influence
species distributions and extinction risk (Jongsomjit et al. 2013). Both contributed to
estimates of nest success and our results were surprising in that air temperatures were
more important for nest survival than metrics of the urban environment that we sampled.
Low impact recreation by pedestrians did not appear to be negatively associated with nest
survival for the more synanthropic species we studied. Other positive aspects of urban
habitat may benefit species that are more sensitive to human disturbance, suggesting a
high potential conservation value of urban parks and open spaces.
36 37 LITERATURE CITED
Aldredge, R.A., LeClair, S.C., Bowman, R. 2012. Declining egg viability explains higher
hatching failure in a suburban population of the threatened Florida scrub-jay
Aphelocoma coerulescens. Journal of Avian Biology 43: 369–375.
AMEC Environment and Infrastructure. 2012. Urban tree canopy assessment Truckee
Meadows (Reno-Sparks, Nevada). 53pp.
Balogh, A.L., Ryder, T.B., Marra, P.P. 2011. Population demography of Gray Catbirds in
the suburban matrix: sources, sinks and domestic cats. Journal of Ornithology 152:
717–726.
Beissinger, S., Cook, M., Arendt, W. 2005. The shelf life of bird eggs: Testing egg
viability using a tropical climate gradient. Ecology 86: 2164–2175.
Both, C., Bouwhuis, S., Lessells, C.M., Visser, M.E. 2006. Climate change and
population declines in a long-distance migratory bird. Nature 441: 81–83.
Both, C., van Asch, M., Bijlsma, R.G., van den Burg, A.B., Visser, M.E. 2009. Climate
change and unequal phenological changes across four trophic levels: constraints or
adaptations? Journal of Animal Ecology 78: 73–83.
Both, C., Van Turnhout, C.A.M., Bijlsma, R.G., Siepel, H., Van Strien, A.J., Foppen,
R.P.B. 2010. Avian population consequences of climate change are most severe for
long-distance migrants in seasonal habitats. Proceedings of the Royal Society BBiological Sciences 277: 1259–1266.
Bradbury, R., Wilson, J., Moorcroft, D., Morris, A., Perkins, A. 2003. Habitat and
weather are weak correlates of nestling condition and growth rates of four UK
farmland passerines. Ibis 145: 295–306.
38 Buckland, S.T. 2001. Introduction to distance sampling: estimating abundance of
biological populations. Oxford University Press, 432 pp.
Chalfoun, A.D. and Martin, T.E. 2007. Assessments of habitat preferences and quality
depend on spatial scale and metrics of fitness. Journal of Applied Ecology 44: 983–
992.
Chalfoun, A.D. and Martin, T.E. 2009. Habitat structure mediates predation risk for
sedentary prey: experimental tests of alternative hypotheses. Journal of Animal
Ecology 78: 497–503.
Chamberlain, D.E., Cannon, A.R., Toms, M.P., Leech, D.I., Hatchwell, B.J., Gaston, K.J.
2009. Avian productivity in urban landscapes: a review and meta-analysis. Ibis 151:
1–18.
Clavero, M., Villero, D., Brotons, L. 2011. Climate change or land use dynamics: do we
know what climate change indicators indicate? PLoS ONE 6.
Cox, W.A., Thompson, III, F.R., Reidy, J.L., Faaborg, J. 2013. Temperature can interact
with landscape factors to affect songbird productivity. Global Change Biology 19:
1064–1074.
Cox, W.A., Thompson, III, F.R., Root, B., Faaborg, J. 2012. Declining Brown-Headed
Cowbird (Molothrus ater) populations are associated with landscape-specific
reductions in brood parasitism and increases in songbird productivity. PLoS ONE 7.
Diffenbaugh, N.S., Giorgi, F., Pal, J.S. 2008. Climate change hotspots in the United
States. Geophysical Research Letters 35.
39 Dreitz, V.J., Conrey, R.Y., Skagen, S.K. 2012. Drought and cooler temperatures are
associated with higher nest survival in Mountain Plovers. Avian Conservation and
Ecology 7.
Fischer, J.D., Cleeton, S.H., Lyons, T.P., Miller, J.R. 2012. Urbanization and the
predation paradox: The role of trophic dynamics in structuring vertebrate
communities. Bioscience 62: 809–818.
Flanders-Wanner, B., White, G., McDaniel, L. 2004. Weather and prairie grouse: dealing
with effects beyond our control. Wildlife Society Bulletin 32: 22–34.
Goddard, A.D. and Dawson, R.D. 2009. Seasonal changes in habitat features influencing
nest survival of sharp-tailed grouse in northeastern British Columbia, Canada.
Ecoscience 16: 476–482.
Gottschalk, T.K. and Huettmann, F. 2011. Comparison of distance sampling and territory
mapping methods for birds in four different habitats. Journal of Ornithology 152:
421–429.
Greno, J.L., Belda, E.J., Barba, E. 2008. Influence of temperatures during the nestling
period on post-fledging survival of great tit Parus major in a Mediterranean habitat.
Journal of Avian Biology 39: 41–49.
Grimmond, C.S.B. and Oke, T.R. 1999. Heat storage in urban areas: Local-scale
observations and evaluation of a simple model. Journal of Applied Meteorology 38:
922–940.
Halfwerk, W., Holleman, L.J.M., Lessells, C.M., Slabbekoorn, H. 2011. Negative impact
of traffic noise on avian reproductive success. Journal of Applied Ecology 48: 210–
219.
40 Hannon, S.J., Wilson, S., McCallum, C.A. 2009. Does cowbird parasitism increase
predation risk to American redstart nests? Oikos118: 1035–1043.
Hepp, G.R. and Kennamer, R.A. 2012. Warm is better: Incubation temperature influences
apparent survival and recruitment of Wood Ducks (Aix sponsa). PLoS ONE 7.
Hollenbeck, J.P., Saab, V.A., Frenzel, R.W. 2011. Habitat suitability and nest survival of
White-Headed Woodpeckers in unburned forests of Oregon. Journal of Wildlife
Management 75: 1061–1071.
Jenerette, G.D., Harlan, S.L., Brazel, A., Jones, N., Larsen, L., Stefanov, W.L. 2007.
Regional relationships between surface temperature, vegetation, and human
settlement in a rapidly urbanizing ecosystem. Landscape Ecology 22: 353–365.
Jokimaki, J., Kaisanlahti-Jokimaki, M., Sorace, A., Fernandez-Juricic, E., RodriguezPrieto, I., Jimenez, M. 2005. Evaluation of the “safe nesting zone” hypothesis across
an urban gradient: a multi-scale study. Ecography 28: 59–70.
Jongsomjit, D., Stralberg, D., Gardali, T., Salas, L., Wiens, J. 2013. Between a rock and a
hard place: the impacts of climate change and housing development on breeding birds
in California. Landscape Ecology 28: 187–200.
Kolada, E.J., Sedinger, J.S., Casazza, M.L. 2009. Nest site selection by Greater SageGrouse in Mono County, California. Journal of Wildlife Management 73: 1333–1340.
Kus, B.E., Peterson, B.L., Deutschman, D.H. 2008. A multiscale analysis of nest
predation on Least Bell’s Vireos (Vireo bellii pusillus). Auk 125: 277–284.
Latif, Q.S., Heath, S.K., Rotenberry, J.T. 2011. An `ecological trap’ for yellow warbler
nest microhabitat selection. Oikos 120: 1139–1150.
41 Lurgi, M., Lopez, B.C., Montoya, J.M. 2012. Novel communities from climate change.
Philosophical Transactions of the Royal Society B-Biological Sciences 367: 2913–
2922.
Lynn, S., Morrison, M., Kuenzi, A., Neale, J., Sacks, B., Hamlin, R., Hall, L. 1998. Bird
use of riparian vegetation along the Truckee River, California and Nevada. Great
Basin Naturalist 58: 328–343.
Mantyka-Pringle, C.S., Martin, T.G., Rhodes, J.R. 2012. Interactions between climate
and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global
Change Biology 18: 1239–1252.
Marzluff, J.M., Withey, J.C., Whittaker, K.A., Oleyar, M.D., Unfried, T.M., Rullman, S.,
DeLap, J. 2007. Consequences of habitat utilization by nest predators and breeding
songbirds across multiple scales in an urbanizing landscape. Condor 109: 516–534.
McKechnie, A.E. and Wolf, B.O. 2010. Climate change increases the likelihood of
catastrophic avian mortality events during extreme heat waves. Biology Letters 6:
253–256.
Møller, A.P., Fiedler, W., Berthold, P. eds. 2010. Effects of climate change on birds.
Oxford University Press, New York, 321pp.
Møller, A.P. 2012. Urban areas as refuges from predators and flight distance of prey.
Behavioral Ecology 23: 1030–1035.
Møller, A.P., Rubolini, D., Lehikoinen, E. 2008. Populations of migratory bird species
that did not show a phenological response to climate change are declining.
Proceedings of the National Academy of Sciences of the United States of America
105: 16195–16200.
42 Morton, M.L. and Pereyra, M.E. 2010. Development of incubation temperature and
behavior in thrushes nesting at high altitude. Wilson Journal of Ornithology 122:
666–673.
Mujahid, A. and Furuse, M. 2009. Oxidative damage in different tissues of neonatal
chicks exposed to low environmental temperature. Comparative Biochemistry and
Physiology A- Molecular and Integrative Physiology 152: 604–608.
Newlon, K.R. and Saab, V.A. 2011. Nest-site selection and nest survival of Lewis’s
woodpecker in aspen riparian woodlands. Condor 113: 183–193.
Newton, I. 2007. Weather-related mass-mortality events in migrants. Ibis 149: 453–467.
Oke, T. 1988. The urban energy-balance. Progress in Physical Geography 12: 471–508.
Olsen, B.J., Felch, J.M., Greenberg, R., Walters, J.R. 2008. Causes of reduced clutch size
in a tidal marsh endemic. Oecologia 158: 421–435.
Peach, W.J., Vincent, K.E., Fowler, J.A., Grice, P.V. 2008. Reproductive success of
house sparrows along an urban gradient. Animal Conservation 11: 493–503.
Pereyra, M. and Morton, M. 2001. Nestling growth and thermoregulatory development in
subalpine Dusky Flycatchers. Auk 118: 116–136.
Pickett, S.T.A., Cadenasso, M.L., Grove, J.M., Boone, C.G., Groffman, P.M., Irwin, E.,
Kaushal, S.S., Marshall, V., McGrath, B.P., Nilon, C.H., Pouyat, R.V., Szlavecz, K.,
Troy, A., Warren, P. 2011. Urban ecological systems: Scientific foundations and a
decade of progress. Journal of Environmental Management 92: 331–362.
Rippon, R.J., Alley, M.R., Castro, I. 2011. Causes of mortality in a nestling population of
free-living hihi (stitchbird-Notiomystis cincta). New Zealand Journal of Zoology 38:
207–222.
43 Robb, G.N., McDonald, R.A., Chamberlain, D.E., Bearhop, S. 2008. Food for thought:
supplementary feeding as a driver of ecological change in avian populations.
Frontiers in Ecology and the Environment 6: 476–484.
Rodewald, A.D. and Kearns, L.J. 2011. Shifts in dominant nest predators along a rural-tourban landscape gradient. Condor 113: 899–906.
Rodewald, A.D., Kearns, L.J., Shustack, D.P. 2011. Anthropogenic resource subsidies
decouple predator-prey relationships. Ecological Applications 21: 936–943.
Ryder, T.B., Reitsma, R., Evans, B., Marra, P.P. 2010. Quantifying avian nest survival
along an urbanization gradient using citizen- and scientist-generated data. Ecological
Applications 20: 419–426.
Schlesinger, M.D., Manley, P.N., Holyoak, M. 2008. Distinguishing stressors acting on
land bird communities in an urbanizing environment. Ecology 89: 2302–2314.
Seto, K.C., Gueneralp, B., Hutyra, L.R. 2012. Global forecasts of urban expansion to
2030 and direct impacts on biodiversity and carbon pools. Proceedings of the
National Academy of Sciences of the United States of America 109: 16083–16088.
Shawkey, M.D., Bowman, R., Woolfenden, G.E. 2004. Why is brood reduction in Florida
scrub-jays higher in suburban than in wildland habitats? Canadian Journal of
Zoology-Revue Canadienne de Zoologie 82: 1427–1435.
Skagen, S.K. and Adams, A.A.Y. 2012. Weather effects on avian breeding performance
and implications of climate change. Ecological Applications 22: 1131–1145.
Smith-Castro, J.R. and Rodewald, A.D. 2010. Effects of recreational trails on Northern
Cardinals (Cardinalis cardinalis) in forested urban parks. Natural Areas Journal 30:
328–337.
44 Thorington, K.K. and Bowman, R. 2003. Predation rate on artificial nests increases with
human housing density in suburban habitats. Ecography 26: 188–196.
Van Heezik, Y., Ludwig, K., Whitwell, S., McLean, I.G. 2008. Nest survival of birds in
an urban environment in New Zealand. New Zealand Journal of Ecology 32: 155–
165.
Vargo, J., Habeeb, D., Stone, Jr., B. 2013. The importance of land cover change across
urban-rural typologies for climate modeling. Journal of Environmental Management
114: 243–252.
Virkkala, R., Heikkinen, R.K., Leikola, N., Luoto, M. 2008. Projected large-scale range
reductions of northern-boreal land bird species due to climate change. Biological
Conservation 141: 1343–1353.
Visser, M.E., van Noordwijk, A.J., Tinbergen, J.M., Lessells, C.M. 1998. Warmer
springs lead to mistimed reproduction in great tits (Parus major). Proceedings of the
Royal Society of London Series B-Biological Sciences 265: 1867–1870.
Wang, J.M. and Weathers, W.W. 2009. Egg laying, egg temperature, attentiveness, and
incubation in the western bluebird. Wilson Journal of Ornithology 121: 512–520.
Webb, D. 1987. Thermal tolerance of avian embryos – a review. Condor 89: 874–898.
White, G. and Burnham, K. 1999. Program MARK: survival estimation from populations
of marked animals. Bird Study 46: 120–139.
Wright, L.J., Hoblyn, R.A., Green, R.E., Bowden, C.G.R., Mallord, J.W., Sutherland,
W.J., Dolman, P.M. 2009. Importance of climatic and environmental change in the
demography of a multi-brooded passerine, the woodlark Lullula arborea. Journal of
Animal Ecology 78: 1191–1202.
45 TABLE 1. A summary of logistic exposure nest survival models for three species along
an urban to rural gradient in Reno and Sparks, Nevada. Models include covariates and
are compared to the null model of constant survival S(.). Individual covariates included
are the temperature deviation from 20-yr normal at hatching with a quadratic term
(hdev2), amount of canopy cover in a 20m radius (canopy 20), and rates of pedestrians at
a study site (peds). An interaction term for species and nest stage (stage*species) was
also included to represent three stages of nesting that differ in daily survival rates. The
lowest AICc value of all candidate models considered was 1444.39.
Model
2
species + stage*species + Hdev + Hdev + peds +
canopy20
1
2
K
3
Deviance
0.923
10
1383.22
5.76
0.052
8
1392.99
9.12
0.010
8
1396.36
9.35
0.009
8
1396.58
10.89
0.004
8
1398.12
species + stage*species
15.23
0.000
6
1406.47
species + stage
21.00
0.000
4
1416.25
species
21.06
0.000
3
1418.32
S(.)
34.70
0.000
1
1435.95
year + site
41.14
0.000
10
1424.36
2
species + stage*species + peds + canopy20
species + stage*species + Cdev + Cdev
species + stage*species + Tdev + Tdev
2
2
Measure of a model distance in AIC units to the best model
Model weight
3
Number of parameters included in the model
4
Measure of model fit to the data
2
wi
0.00
species + stage*species + Hdev + Hdev
1
ΔAICc
4
46 FIGURE 1. Study area location (star) in the western United States. Enlarged area of
Reno and Sparks, Nevada with study sites (black) connected by the Truckee River (black
line). Location of the Reno/Cannon International Weather Station (Co-op ID: 266779) is
marked by the +. Enlarged images of two study sites demonstrate the within-site
variation in land cover and among-site variation along the urban to rural gradient.
47 FIGURE 2. Weekly averages of daily maximum temperatures in the study area during
the breeding season for 2009 – 2012. Upper horizontal dashed line marks a temperature
threshold of 27 °C, the upper boundary of physiological zero. Lower horizontal dashed
line marks the temperature threshold for 15 °C, below which nestlings are likely to be
negatively impacted by cold.
FIGURE 3. Temperature deviation from the 20-yr norm at the time of clutch initiation
for three bird species nesting in urban habitat over a four-year study period.
48 49 FIGURE 4A-B. Brood parasitism of grosbeak nests by Brown-headed Cowbirds in A) 36
grosbeak nests that either fledged young or failed, and B) the number of cowbird eggs
deposited at a range of temperature deviations from the 20-yr normal for the five days
during egg laying.
50 FIGURE 5. Fitted curve of nestling survival probability estimates (± SE) for temperature
deviations from normal after hatching for three species breeding in urban habitat over a
four-year study period.
51 FIGURE 6A-B. Differences in the amount of canopy cover within a 20m radius, and
rates of pedestrian traffic, between the nests of three species that fledged young or failed
in urban habitat over a four-year study period.
52 NEST HABITAT SHIFTS TO SUITABLE MICROCLIMATES IN RESPONSE TO
WARMER TEMPERATURES
MILES E. BECKER AND PETER J. WEISBERG
ABSTRACT
Bird populations at risk of losing access to their historic bioclimatic envelope may benefit
from increased spatial heterogeneity of within-territory microclimates, provided those
microclimates function to buffer air temperatures and they are used as thermal refuges.
We monitored the nests of three species in an arid ecosystem to investigate the
relationship between spatial microclimate heterogeneity, variation in air temperature, and
shifts in nest locations. Microclimate was recorded in spring and summer at sites
surrounded by four land cover types with different biophysical properties in urban
habitat. The same four habitat variables were sampled at nests over a four year
monitoring period. Microsites with more lawn and understory vegetation had cooler
daily minimum temperatures. Lower levels of canopy cover and higher levels of bare
ground increased daily maximum temperatures. High levels of impervious surface
moderated temperatures by decreasing daily maximum and increasing daily minimum
temperatures. Habitat within a 10m radius explained more of the variance in
microclimate than at scales of 20m and 100m. Two generalist species shifted nest site
microhabitat at warmer temperatures before clutch initiation to locations with less bare
ground and more impervious surfaces. Nest habitat use was apparently sensitive to air
temperatures, suggesting birds are able to compensate for unfavorable temperatures by
using appropriate microclimates. More bird populations are likely to experience
unfavorable regional temperatures due to climate change, increasing the importance of
conserving spatial heterogeneity in habitat features that provide a broad range of
microclimates.
53 54 INTRODUCTION
Temperature is a defining characteristic of the fundamental niche of a species
(Kearney and Porter 2004, Monahan 2009) and often a primary influence on local habitat
use (Monasterio et al. 2009, Speed et al. 2012). At coarse regional scales, many species
are projected to lose their preferred thermal envelope from their current range due to
global climate change (Domisch et al. 2013, Keith et al. 2008, Thuiller et al. 2005), even
within the boundaries of currently protected reserves (Ackerly et al. 2010, Araujo et al.
2011, Virkkala et al. 2013). Broadscale climate modeling overlooks microhabitat and
microclimate that may be especially important for species with limited ability to shift
range boundaries (Scherrer and Koerner 2010). As temperature resources shrink or shift,
spatial thermal heterogeneity at fine scales of less than 100m may facilitate species
persistence in current ranges by providing temporary in situ thermal refuges. Spatial
heterogeneity is advantageous for population stability in theory (Oliver et al. 2010,
Ruokolainen et al. 2011), but the extent to which at-risk populations make use of thermal
refuges through facultative behavioral adjustments is not well known.
Identifying fine-scale habitat features that correspond to microclimatic value is a
challenge. A habitat patch or individual structure used as a thermal refuge must differ
from surrounding temperatures and be selected by an organism for its thermal advantages
(Keppel and Wardell-Johnson 2012). Thus, there are two steps to identifying thermal
refuges. First, spatial variation in microclimate needs to be explained by habitat
heterogeneity. Spatial heterogeneity in temperature can be related to distance from water,
topography, wind exposure, vegetation structure, and habitat type (Ashcroft et al. 2009,
55 Holden et al. 2010, Suggitt et al. 2011). Relative spatial differences in temperature may
be more pronounced in warmer seasons and during the day than at night (Suggitt et al.
2011), potentially creating a dynamic thermal value for static structural features.
An equally necessary second step is to link thermal heterogeneity to resource
selection by the species of interest. Many bird populations are declining in association
with climate change (Both et al. 2010, Møller et al. 2008) and some are expected to lose
their bioclimatic envelope from their current range (Virkkala et al. 2008). Birds may use
behavioral thermoregulation to avoid heat stress, such as bobwhites resting at cooler
microsites during the day in summer (Hiller and Guthery 2005), or to reduce the cost of
thermoregulation in winter, as suggested by increased bird abundance in forests with
more solar radiation penetrating the canopy (Huertas and Diaz 2001). Selecting habitat
with favorable microclimate can have fitness consequences for survival of adults (Patten
et al. 2005) that may ultimately impact population dynamics.
Besides adult survival, another avian vital rate likely to be influenced by
microclimate is reproduction. Hatching success and nest fate are associated with air
temperatures in some species (Aldredge et al. 2012, Greno et al. 2008, Chapter 1). Nest
site selection may be especially sensitive to microclimate because the location of a nest is
immobile over a range of temperatures during the 30 to 80 day nest cycle. Studies on
nest site microclimate are often limited to considerations of nest position and structure.
Nest orientation can influence daytime temperatures at the nest (Lloyd and Martin 2004)
and cavity nesters may select warmer cavities (Maziarz and Wesolowski 2013, Rhodes et
al. 2009) or use more nest insulation at colder temperatures (Deeming et al. 2012).
56 Vegetation immediately adjacent to or in contact with the nest can shield it from wind or
solar radiation (Kim and Monaghan 2005, Tieleman et al. 2008).
Examples of preferences for warmer nest structures are evidence of microclimate
management at the level of the nest itself. Open-cup nesters are additionally expected to
control microclimate to some extent by adjusting nest location in the surrounding land
cover, yet empirical evidence for shifts in nest site selection in response to temperature is
surprisingly lacking from the extensive body of literature on bird-habitat associations.
The aim of this study was to identify resource selection of thermal refuges for nest sites.
We first generated a series of hypotheses and predictions about the potential influence of
microclimate on nest habitat over the season that could be distinguished from the effects
of other factors. We then established the relationship between microclimate and
microsite with fine-scale temperature data loggers. To assess the use of microclimates
over time, we sampled nest habitat for three songbird species over four breeding seasons
in a region with high climatic variability that is forecasted to become warmer.
METHODS
Hypothesized Effects
There are several ways in which temperature can influence nest habitat use
independently or in addition to other factors driving nest site selection. We generated
several hypothesized relationships (Figure 1) where a change in nest habitat with air
temperature would be observed. To facilitate the isolation of temperature influences
from other factors, the breeding season was divided into three time periods of early, mid-,
57 or late season nesters. Due to the highly variable spring temperatures in the study area, it
was possible for late nesters to experience colder temperatures at the time of clutch
initiation than nests started early in the season. As a result, we were effectively able to
separate out temperature effects from other time varying influences on nest habitat
selection, like predation risk or food availability. The null hypothesis was that nest
habitat did not change over the season or with temperature (Figure 1A). Nest locations
may have shifted over time but not a temperature range (Figure 1B), or the opposite could
also occur (Figure 1C). Both time and temperature could contribute an additive effect to
habitat use (Figure 1D). Temperature effects could be dependent on the timing of nesting
and result in an interaction that obscures the individual effects of each (Figure 1E) or
increases the strength of the association between temperature and nest habitat shifts
(Figure 1F). We expected any constraints on using nest habitat for optimal microclimate
from other factors, like low predation risk, to be manifested as an interaction illustrated in
Figure 1E and 1F.
Any temperature effects should stand out from the effects of other time varying
factors, provided potentially confounding factors are uncorrelated with temperature. For
example, canopy cover may prevent nests from overheating in hot weather while
simultaneously reducing the risk of predators detecting the nest. If predator activity also
increases when it is hot, then the effects of the two variables cannot be independently
quantified. However, identical responses to multiple stressors are unlikely to occur
across all land cover variables considered.
Study area
58 The study area in northern Nevada is on the border of the Great Basin and eastern
Sierra Nevada where there are four distinct seasons. The regional climate is
characterized by cold winters (January high of 7° C) and hot summers (July high of 33°
C) with most precipitation falling as snow in winter. Intra-seasonal temperatures during
the breeding season in early spring and summer can be unpredictable over one to two
weeks in a single year (Figure 2).
The study sites pass through Reno and the adjoining city of Sparks, two mid-sized
cities that supported a combined population of 315,485 people in 2010 (US Census
2010). Rapid outward growth from the city centers has led to the expansion of suburbs
and housing developments into sagebrush habitat adjacent to riparian woodlands along
the Truckee River that runs west to east through town. The urban ecosystem is
particularly well suited to studies on microclimate use because the habitat is highly
heterogeneous at fine-scales, increasing the opportunity for microclimate heterogeneity at
the territory level. There are also strong barriers to dispersal from reserves in urban
habitat for birds and other species (Delaney et al. 2010, Kennedy and Marra 2010,
Tremblay and St Clair 2011) that prevent an individual from tracking its preferred
thermal niche across the landscape and perhaps strengthen pressure on behavioral
thermoregulation.
Study sites were along a 15.7 km west to east transect that intersected rural,
suburban, and urban zones. We selected eight municipal parks (mean size = 9.9 ha) with
some native riparian vegetation next to the Truckee River and maintained by local
municipalities for public use. The heterogeneity of fine-scale land cover types within the
parks provided a wide selection of diverse microsites in a small geographic area.
59 Microclimate data
We deployed 95 iButton temperature data loggers (Dallas
Semiconductor/MAXIM, Dallas, TX, USA) in eight study sites in spring and summer
2011 to encompass the majority of the breeding season for my study species. The time
period analyzed was 12 weeks from April 21 to July 13. Many of the deployed data
loggers were missing or moved at the time of retrieval and only 48 were used for the
microclimate analysis. All iButtons were placed 1.5m off the ground and under the bark
on the NNW to NNE facing side of a tree. Data loggers oriented north on a tree are
shielded from direct radiation (Holden et al. 2010). Air temperatures were recorded at 4hr intervals and used to calculate the temperature variables of daily temperature
maximum (Tmax) and daily temperature minimum (Tmin), which are more likely than
mean temperature to be influenced by local habitat (Suggitt et al. 2011). Tmax and Tmin
are also more biologically meaningful metrics because organisms experience a full range
of temperatures in a 24-hr period, not the mean.
Only one year of microsite temperatures was recorded even though four years of
microsite nest habitat data were sampled. There were no a-priori reasons to expect the
relationship between microhabitat and microclimate in 2011 to change in other years,
although the magnitude may have changed. Temperatures in the study area recorded by
the local National Weather Station tower showed strong inter-annual temperature
differences over the 4-yr study period (Figure 2). Two years, 2010 and 2011, were colder
springs and microsite temperatures may have differed less but any thermal refuges should
serve the same function from year to year.
60 The habitat variables we measured at each iButton location that may influence
microclimate were land cover (bare ground, impervious surface, canopy cover, and
understory vegetation) and distance to river. Land cover was calculated for 10m, 20m,
and 100m radius buffer centered on the sampling point using ArcGIS software (ArcMap
10, ESRI, Redlands, California, USA). A base map of 1m resolution orthophotographs
captured in summer 2010 by the National Agriculture Imagery Program (NAIP) was used
to classify land cover types with a geographic object-based image analysis. A point-based
accuracy assessment of 273 randomly selected points yielded an overall accuracy of 94%
(AMEC 2012). Water surfaces were absent from almost all locations at the 10m and 20m
scales, and we excluded this land cover type from the analyses. The west to east slope
over the entire study area was 0.4% and every study site was essentially flat, so no
topographical features were used as predictor variables.
Nest site selection
The nests of three native species, American Robin (Turdus migratorius, hereafter
robin), Black-headed Grosbeak (Pheucticus melanocephalus), grosbeak), and Mourning
Dove (Zenaida macroura, dove) were observed in seven study sites for four years from
2009 to 2012. Robins build heavy mud and grass nests in a variety of substrates,
including human structures. Doves are also nest habitat generalists and construct nests
made of a weak twig platform. Grosbeaks are riparian specialists and have the narrowest
breadth of nest site substrates of the three study species. In our study sites, they
invariably build nests in dense vegetation close to the river. Doves begin breeding the
earliest in late March, robins in mid- to late-April, and grosbeaks in mid-May.
61 We started searching for nests in late March to early April at the start of the
breeding season. Only active nests – indicated by an incubating female, the presence of
eggs or nestlings, or parents delivering food to the nest – and nests with a known clutch
initiation date were considered for analysis (N = 320). The same habitat variables
sampled at iButton locations were calculated for each nest at the same three scales. An
additional habitat variable, nest height, was included since vertical variation may
represent a gradient of predation risk (van Heezik et al. 2008), human disturbance
(Smith-Castro and Rodewald 2010), or air temperature (Suggitt et al. 2011).
Predator Activity
Birds are a major nest predator in urban areas (Marzluff et al. 2007, Thorington
and Bowman 2003). We counted corvids along transects at each of the study sites for an
index of predator activity at different air temperatures. The perpendicular distance from
an observer on line transects through the entire length of each site to a detected bird or
predator was measured with a laser rangefinder (Sport 450, Bushnell, Overland Park,
Missouri, USA). Observations of corvids were made between 0630 – 1000 and the
sampling order between sites was rotated at every visit to avoid a temporal bias. We
repeated transects every five to eight days for eight replicates per site.
The number of corvids observed at a site was divided by the area (ha) of the site
for an activity index. We were interested in within-site variation in corvid counts over
the eight survey intervals, which assumes that detection probability does not change with
time but allows for different detection probabilities between sites. The transect survey
period started after the breeding season when territories were established and ended
62 before most nests fledged, reducing the chance that differences in corvid counts were due
to movement in or out of the study site or the appearance of juveniles.
ANALYSES
Microclimate
We analyzed changes in temperatures at iButton locations over time and by cover
type in a repeated measures generalized linear model with weekly intervals as the withinsubjects factor and land cover category or distance to river as the between subjects factor.
Separate models were run for the four types of land cover at the three spatial scales and
for two temperature response variables, Tmax and Tmin. Daily Tmax and Tmin were
averaged over the seven-day time interval. We assigned treatment levels of a cover type
to each iButton by binning values into three categorical groups of low, mid-level, and
high using the 33rd, 66th, and 100th population percentiles as treatment threshold values.
If at least 20% of the iButtons had a value of zero for that cover type, then a 4th group of
“none” was added and the percentiles calculated from the remaining non-zero values. At
least half of the iButtons had no bare ground or impervious surfaces at 10m and 20m, and
only the non-zero values were included in the analyses to maintain a balanced design.
Distance to river was similarly binned into three levels of near, mid-range, and far.
Nest Site Selection
The analysis of nest site selection treated temperature and time as predictor
variables and microsite features at the nest as continuous response variables. Daily Tmax
63 values in the study area for five days before clutch initiation (Tmax5) were binned into
three fixed treatment levels of temperature: cold, mild, and warm. Threshold values
between temperature levels were species-specific and evenly divided over the
temperature range experienced at the start of nesting over the entire study period (Table
1). Five to seven day temperature ranges have been shown to be good predictors of
clutch initiation and nest structure in other species (Wiebe and Gerstmar 2010, Deeming
et al. 2012). Ambient Tmax5 and Tmin5 in the study area were highly correlated
(Pearson’s = 0.879, P < 0.001), so only Tmax5 was used as a predictor variable. Tmax
temperatures were provided by data from the Reno/Tahoe National Weather Station,
located close to the study sites. Three treatment levels of timing of nesting (early, mid,
late) were calculated from percentiles by species and year as a relative measure of when a
bird began nesting in that year compared to conspecifics (Table 1).
Variation in the correlated multiple response variables at a nest was modeled in a
2 x 2 MANOVA. The assigned levels of temperature and timing of breeding were
species-specific and effectively standardized treatment levels across species. An initial
model with species as a fixed factor did not show any interaction between species and
time (F20,963 = 1.361, P = 0.133) or species and Tmax5 (F20,963 = 1.152, P = 0.289). The
similarity in robin and dove nest habitat and distinctly different grosbeak nest habitat
(Table 1) was supported by a post-hoc Tukey’s HSD multiple comparisons test. Due to
the smaller number of nests sampled for grosbeaks and their distinct difference from the
other species, we modeled their nest selection separately and pooled robin and dove nests
together in the same model. The statistical approach requires independence between
samples in each of the treatments, and it is possible that nests initiated later in the season
64 were re-nesting attempts by the same unmarked female. We avoided this potential bias
by not including multiple nesting attempts in the same territory.
Predator Activity
Corvid counts for a survey interval were paired with ambient Tmax in the study
area on that day. Temperatures over the range of those experienced during predator
monitoring transects were binned into cold, mild, and warm groups following the same
threshold values as temperatures used for predicting nest site selection by the study
species. Within-site differences in corvid counts at each interval and temperature were
analyzed in a repeated measures ANOVA with the activity index as a response variable,
temperature groups as the treatment level, and transect interval as the repeated measure.
RESULTS
All values are mean ± SD, unless otherwise noted.
Microclimate
Microsites with iButtons showed strong seasonal variation in Tmax and Tmin in
2011. Maximum daily temperatures increased from 13.8 ± 2.2 °C in the fourth week of
April to 27.0 ± 5.0 °C in the second week of July. Tmin also increased over the same
time period from 4.7 ± 1.4 °C to 13.8 ± 2.4 °C. There was substantial spatial variation in
microclimate. Within a single study site in April, Tmax differed between iButtons by 4.3
± 3.4 °C and by 3.3 ± 1.8 °C for Tmin. By July, spatial temperature differences within a
65 site had tripled to 14.0 ± 3.0 °C for Tmax and risen slightly to 5.7 ± 1.7 °C for Tmin.
Overall, spatial variation in microclimate was amplified during the warmer part of the
season and day.
The propensity of microsites to gain or lose heat was related to surrounding
habitat. Land cover types functioned differently in regulating microclimate. The most
abundant land cover type was vegetation (understory shrubs or lawn). Within a 20m
radius, vegetation did little to change daily Tmax (F2,45 = 1.999, P = 0.147), but
microsites with vegetation cover above 30% were cooler at night (F2,45 = 3.776, P =
0.030; Figure 3A). Impervious surfaces was also associated with Tmin warming at night
for surface areas covered by over 24% (F2,22 = 3.551, P = 0.046) but the relationship was
not significant for Tmax (F2,22 = 2.742, P = 0.086; Figure 3B). Microsites with more than
10% canopy cover were cooler in the day (F2,45 = 4.305, P = 0.019), but canopy cover
was not related to Tmin (F2,45 = 0.717, P = 0.494; Figure 3C). Within a 10m radius, bare
ground was related to higher Tmax (F2,20 = 3.866, P = 0.038) but the same amounts of
over 26% land cover did not have a significant relationship with Tmin (F2,20 = 1.641, P =
0.219; Figure 3D). Distance to river was not associated with Tmax or Tmin (Tmax: F2,45
= 1.04, P = 0.362; Tmin: F2,45 = 1.718, P = 0.191).
The direction of thermal influence for each cover type did not change over time,
but the relative magnitude of the variation did (Figures 3A-D). Microclimate variation
between levels of any one land cover type was much more pronounced at air
temperatures over 22 °C Tmax and 10 °C Tmin, starting by mid-June in 2011.
Microhabitat at the 10m radius scale explained more variance in microclimate (10m:
Wilk’s Λ = 0.20; 20m: Wilk’s Λ = 0.30; 100m; Wilk’s Λ = 0.28). The relative
66 importance of land cover differed slightly with each scale. At a 10m radius, Tmax
significantly increased with bare ground and Tmin decreased with vegetation (F2,45 =
6.587, P = 0.003). The variables with significant relationships to Tmax and Tmin at a
20m radius were canopy, vegetation, and impervious surfaces. Only impervious surface
significantly decreased Tmax at the broadest scale considered, a 100m radius (F2,45 =
3.25, P = 0.048).
Nest site selection
Over the four-year study period, we observed nests initiated from March 16 to
July 2. Temperatures at the time of nesting ranged from 8.5 – 32.7 °C for Tmax and -2.2
– 14.5 °C for Tmin. Variation in microhabitat at robin and dove nests was better
explained by lay date (Wilks Λ = 0.859) than by the temperatures at the time of nesting
(Wilks Λ = 0.955) for all spatial scales. Late nesters were more likely to construct nests
in areas with more vegetation (F2,268 = 9.690, P < 0.001; Figure 4A) and less impervious
surface (F2,268 = 8.728, P < 0.001; Figure 4B) at 20m, and more bare ground (F2,268 =
5.657, P = 0.004; Figure 4C) in a 100m radius as predicted in Figure 1B. Canopy cover
was consistent over time and the temperature range, matching the hypothesized
relationship in Figure 1A (F2,268 = 0.312, P = 0.732; Figure 4D). For robins and doves,
the variance explained by the three spatial scales of microhabitat were fairly similar
(10m: Wilks Λ = 0.95; 20m: Wilks Λ = 0.94; 100m: Wilks Λ = 0.92).
Although weak, the effect of temperature on nest site selection was significant for
bare ground in a 100m radius (F2,268 = 5.717, P = 0.004). Independently of timing, nests
started at warmer temperatures had less bare ground (Figure 4D), following the predicted
67 pattern of additive effects illustrated in Figure 1D. Nests constructed at colder
temperatures were significantly more likely to have less impervious surfaces within 20m
(F2,268 = 3.114, P = 0.046; Figure 4B) at least during the early and middle parts of the
season, similar to the prediction of a change in the magnitude of a temperature effect
illustrated in Figure 1F.
Timing of breeding and temperature explained more variance in nest habitat use
for grosbeaks than for robins and doves (Time: Wilks Λ = 0.660; Tmax5: Wilks Λ =
0.757). The only response variable that significantly changed over time for grosbeaks
was nest height (F2,34 = 3.832, P = 0.032). Average height of nests initiated in the middle
of the grosbeak season was 1.7m lower than earlier nests and 0.9m lower than nests
constructed later.
Predator Activity
The most frequently observed corvids at the study sites were Western Scrub Jay
(Aphelocoma californica), Steller’s Jay (Cyanocitta stelleri) and Black-billed Magpie
(Pica hudsonia). Counts of corvids per hectare along transects in the sites were highest at
cold temperatures below 19 °C (0.53 ± 0.48) and similar in mild temperatures (0.30 ±
0.21) and warm temperatures above 26 °C (0.31 ± 0.40; Figure 5). The differences were
not statistically significant (F2,14 = 1.990, P = 0.174).
DISCUSSION
Microclimate
68 Thermal refuges were identified in this study as cooler habitat patches with less
bare ground and more impervious surfaces and that were used as nest sites by doves and
robins at warmer temperatures. Changes in microhabitat were associated with larger
differences in microclimate at warmer temperatures. The opposite has been observed at a
broader scale, where habitat heterogeneity was associated with warming of minimum
temperatures and in the coldest season (Ashcroft et al. 2009). However, similar results to
our study were found for open habitats with less canopy cover, and spatial variation in
temperature was also more evident in warmer seasons (Suggitt et al. 2011). Bare ground
and open habitat allow direct solar radiation to warm air and soil. Shading by canopy
cover intercepts solar radiation and its effects are more likely to diminish at night or in
regions with abundant cloud cover. Low growing vegetation, which often consisted of
lawn and turf in this study, cannot effectively shade habitat patches at nest level and so
did not influence Tmax. Most of the non-canopy vegetation in our study was irrigated
and water retention in soils may have contributed to lower temperatures at night.
Unexpectedly, the land cover type that moderated daily air temperature ranges the
most was impervious surface. Light colored rooftops with high albedo have the ability to
reflect light at sufficient levels to effectively cool surrounding areas (Taha 1997).
Microsites with data loggers in this study were in parks and fine-scale impervious
surfaces were limited to paved roads and small utility buildings. Therefore, the thermal
properties of concrete and asphalt were likely responsible for the observed microclimate
variation. Thermal inertia of man-made materials in cities is evident from a peak in the
Urban Heat Island effect at sunset (Pickett et al. 2011), not at the time of peak air
temperatures. The latent heating effect from impervious surfaces suggests they absorb
69 heat in the day, potentially creating a heat sink, and release stored heat later in the day,
serving as a heat source. The amount of heat absorbed by paved surfaces before releasing
heat is probably limited and the temporal dynamics of heat transfer may change at the
height of summer.
Land cover within a 10m radius of iButton locations explained most of the
variance in microclimate. Other microclimate studies across a heterogeneous landscape
did not test explicitly for scale effects, but found spatial variation in temperatures when
iButtons were deployed at distances of approximately 40m (Suggitt et al. 2011) to over a
kilometer (Ashcroft et al. 2009, Holden et al. 2010). The spatial scale important for
microclimate at a nest will probably differ with habitat and should be evaluated prior to
identifying thermal refuges.
Nest sites
The timing of breeding relative to other conspecific nesters explained more
variance in nest site selection than did air temperature, possibly due to other time-varying
influences on nesting that were not accounted for in this study. My study design was not
able to rule out the possibility that nest site selection by the three species was influenced
by other factors besides temperature. Nest site selection may be a response to perceived
predation risk or actual nest fate (Chalfoun and Martin 2010). Nesting close to food
resource rich patches may also pay off to meet the high nutrient demand by nestlings
while minimizing adult energy expenditure on longer-distance foraging flights.
Phainopepla (Phainopepla nitens) nested in patches with greater abundance of their
preferred food, berries of the desert mistletoe (Phoradendron californicum), within a 5m
70 radius and a 11.3m radius (Crampton and Sedinger 2011). Competition for high quality
habitat can also force individuals with a lower competitive ability, like juveniles or latearriving migrant breeders, into low quality habitat (Loehle 2012).
Air temperature has the potential to influence predator activity and prey
availability for some species, which could confound temperature effects on nest
microhabitat use. Ectothermic predators, like snakes, may be more active and more of a
threat to bird nests in warmer weather (Morrison and Bolger 2002). Corvids, a common
nest predator in urban areas (Marzluff et al. 2007, Thorington and Bowman 2003), are
locally abundant in the study area (Trammell and Bassett 2012). Detection rates of
corvids in the study sites did not increase on warmer days, making it less likely that
temperature influenced nest predator activity. Robins primarily forage for soil
macroinvertebrates which could be less accessible at colder temperatures, but lawns and
short grass are the dominant land cover in almost all robin territories, possibly sustaining
a high prey abundance even at low temperatures. Doves consume seeds and other plant
materials, which are available independently of short-term temperature variability,
making it unlikely that food availability was confounded with temperature in nest habitat
use by doves and robins.
The effect of air temperature on nest site selection may have been stronger in a
warmer series of years; temperatures at the time of clutch initiation during the four-year
study period were cooler than the 20-yr normal (Chapter 1). Air temperatures in the
study area also peak in late June and July, after nest searching ended but before the
breeding season ended. In an extreme desert environment where maximum air
temperatures later in the breeding season reach 47.6 °C, Hoopoe Larks (Alaemon
71 alaudipes) consistently changed nest site locations as the season progressed (Tieleman et
al. 2008). Given that nest site selection for favorable microclimates may have intensified
later in the summer at warmer temperatures, it would be useful to study nest microclimate
over a wider range of air temperatures.
Despite the effects being relatively weak, air temperatures were related to nest site
selection for robins and doves. At warmer temperatures, both species were more likely to
use nest sites with less bare ground land cover. At cooler temperatures, they nested at
sites with less impervious surface land cover. The iButton data show that microsites with
more bare ground are warmer and sites with more impervious surfaces are cooler. The
results suggest robins and doves adjusted nest microclimate in response to air
temperatures in addition to other factors, as predicted in the hypothesized relationships
illustrated in Figure 1.
Grosbeaks changed only the height of their nests in the middle of the season, the
biological significance of which is difficult to determine. However, it is unlikely that
grosbeaks are completely unresponsive to air temperatures. Instead, robins and doves
may be able to follow their preferred thermal niche across multiple habitat structures and
vegetation compositions because they are habitat generalists. Grosbeaks are habitat
specialists that may be confined to one habitat type, able to capitalize on spatial variation
in temperatures only from the presence of topographic heterogeneity, which did not exist
in my study sites.
Conclusions
72 Changing air temperatures will likely become an increasingly important constraint
for birds nesting in the Great Basin, the southwestern United States, and other rapidly
warming parts of the world. One solution to shrinking bioclimatic envelopes is the
preservation of large areas of contiguous habitat or corridors connecting a network of
smaller reserves to facilitate species dispersal and migration to preferred climatic habitat
(Bernazzani et al. 2012, Hannah 2011, Nunez et al. 2013). However, such broad scale
management actions may not be economically or logistically feasible for many reserves
in disturbed landscapes with a matrix of other land uses, including urban habitat (Rudd et
al. 2002). Instead, fine-scale in situ habitat heterogeneity and thermal refuges may be a
better strategy for species persistence and ecosystem resilience (Ackerly et al. 2010).
Temporary behavioral thermoregulation is apparent in ectotherms, such as reef
sharks swimming to warmer waters to maintain higher metabolism (Speed et al. 2012) or
ungulates like bighorn sheep that may seek out shade under tree canopies or in caves at
warmer times of the day in desert environments (Cain et al. 2008). Such short-term
temporary thermal refuges may influence fitness and habitat use, but microclimate may
be more important for fixed nest locations that could benefit from being buffered by daily
and weekly temperature fluctuations. Despite the apparent need to understand the
influence of microclimate on nest habitat use, few studies have done so in the context of
seasonal shifts. Two habitat generalists in this study shifted to more favorable nest
microclimates at warmer temperatures, supporting the value of fine-scale habitat
heterogeneity. It should be recognized that loss of suitable microclimates can result from
either regional long-term climate change or localized vegetation structural changes from
land use change. In this study, the maximum temperature range within a single urban
73 reserve (14 °C) was much greater than the predicted temperature change forecasted for
the study area of a few degrees by the end of the century (Diffenbaugh et al. 2008). Since
spatial thermal variance is so much greater than long-term annual variance, modification
of land cover by land use change may be a more important ecosystem stressor than
climate change. However, land use change, including urban development, also has the
potential to create novel microclimates and buffer organisms against any negative
impacts of climate change. Mitigation strategies that use management of local habitat to
counteract climate change effects warrant further study (Clausen et al. 2013).
74 LITERATURE CITED
Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R.,
Kraft, N.J.B. 2010. The geography of climate change: implications for conservation
biogeography. Diversity and Distributions 16: 476–487.
Aldredge, R.A., LeClair, S.C., Bowman, R. 2012. Declining egg viability explains higher
hatching failure in a suburban population of the threatened Florida scrub-jay
Aphelocoma coerulescens. Journal of Avian Biology 43: 369–375.
AMEC Environment and Infrastructure. 2012. Urban tree canopy assessment Truckee
Meadows (Reno-Sparks, Nevada). 53pp.
Araujo, M.B., Alagador, D., Cabeza, M., Nogues-Bravo, D., Thuiller, W. 2011. Climate
change threatens European conservation areas. Ecology Letters 14: 484–492.
Ashcroft, M.B., Chisholm, L.A., French, K.O. 2009. Climate change at the landscape
scale: predicting fine-grained spatial heterogeneity in warming and potential refugia
for vegetation. Global Change Biology 15: 656–667.
Bernazzani, P., Bradley, B.A., Opperman, J.J. 2012. Integrating climate change into
habitat conservation plans under the U.S. Endangered Species Act. Environmental
Management 49: 1103–1114.
Cain, J.W., Jansen, B.D., Wilson, R.R., Krausman, P.R. 2008. Potential thermoregulatory
advantages of shade use by desert bighorn sheep. Journal of Arid Environments 72:
1518–1525.
Both, C., Van Turnhout, C.A.M., Bijlsma, R.G., Siepel, H., Van Strien, A.J., Foppen,
R.P.B. 2010. Avian population consequences of climate change are most severe for
75 long-distance migrants in seasonal habitats. Proceedings of the Royal Society BBiological Sciences 277: 1259–1266.
Chalfoun, A.D. and Martin, T.E. 2010. Facultative nest patch shifts in response to nest
predation risk in the Brewer’s sparrow: a “win-stay, lose-switch” strategy? Oecologia
163: 885–892.
Clausen, K.K., Stjernholm, M., Clausen, P. 2013. Grazing management can counteract
the impacts of climate change-induced sea level rise on salt marsh-dependent
waterbirds. Journal of Applied Ecology 50: 528–537.
Crampton, L.H. and Sedinger, J.S. 2011. Nest-habitat selection by the phainopepla:
congruence across spatial scales but not habitat types. Condor 113: 209–222.
Deeming, D.C., Mainwaring, M.C., Hartley, I.R., Reynolds, S.J. 2012. Local temperature
and not latitude determines the design of Blue Tit and Great Tit nests. Avian Biology
Research 5: 203–208.
Delaney KS, Riley SPD, Fisher RN. 2010. A rapid, strong, and convergent genetic
response to urban habitat fragmentation in four divergent and widespread vertebrates.
PLoS ONE 5: e12767.
Diffenbaugh, N.S., Giorgi, F., Pal, J.S. 2008. Climate change hotspots in the United
States. Geophysical Research Letters 35.
Domisch, S., Araujo, M.B., Bonada, N., Pauls, S.U., Jaehnig, S.C., Haase, P. 2013.
Modelling distribution in European stream macroinvertebrates under future climates.
Global Change Biology 19: 752–762.
76 Greno, J.L., Belda, E.J., Barba, E. 2008. Influence of temperatures during the nestling
period on post-fledging survival of great tit Parus major in a Mediterranean habitat.
Journal of Avian Biology 39: 41–49.
Hannah, L. 2011. Climate change, connectivity, and conservation success. Conservation
Biology 25: 1139–1142.
Hiller, T. and Guthery, F. 2005. Microclimate versus predation risk in roost and covert
selection by bobwhites. Journal of Wildlife Management 69: 140–149.
Holden, Z.A., Crimmins, M.A., Cushman, S.A., Littell, J.S. 2011. Empirical modeling of
spatial and temporal variation in warm season nocturnal air temperatures in two North
Idaho mountain ranges, USA. Agricultural and Forest Meteorology 151: 261–269.
Huertas, D. and Diaz, J. 2001. Winter habitat selection by a montane forest bird
assemblage: the effects of solar radiation. Canadian Journal of Zoology-Revue
Canadienne de Zoologie 79: 279–284.
Kearney, M. and Porter, W. 2009. Mechanistic niche modelling: combining physiological
and spatial data to predict species’ ranges. Ecology Letters 12: 334–350.
Keith, D.A., Akcakaya, H.R., Thuiller, W., Midgley, G.F., Pearson, R.G., Phillips, S.J.,
Regan, H.M., Araujo, M.B., Rebelo, T.G. 2008. Predicting extinction risks under
climate change: coupling stochastic population models with dynamic bioclimatic
habitat models. Biology Letters 4: 560–563.
Kennedy, C.M. and Marra, P.P. 2010. Matrix mediates avian movements in tropical
forested landscapes: Inference from experimental translocations. Biological
Conservation 143: 2136–2145.
77 Keppel, G. and Wardell-Johnson, G.W. 2012. Refugia: keys to climate change
management. Global Change Biology 18: 2389–2391.
Kim, S. and Monaghan, P. 2005. Interacting effects of nest shelter and breeder quality on
behaviour and breeding performance of herring gulls. Animal Behaviour 69: 301–
306.
Lloyd, J. and Martin, T. 2004. Nest-site preference and maternal effects on offspring
growth. Behavioral Ecology 15: 816–823.
Loehle, C. 2012. A conditional choice model of habitat selection explains the source-sink
paradox. Ecological Modelling 235: 59–66.
Marzluff, J.M., Withey, J.C., Whittaker, K.A., Oleyar, M.D., Unfried, T.M., Rullman, S.,
DeLap, J. 2007. Consequences of habitat utilization by nest predators and breeding
songbirds across multiple scales in an urbanizing landscape. Condor 109: 516–534.
Maziarz, M. and Wesolowski, T. 2013. Microclimate of tree cavities used by Great Tits
(Parus major) in a primeval forest. Avian Biology Research 6: 47–56.
Møller, A.P., Rubolini, D., Lehikoinen, E. 2008. Populations of migratory bird species
that did not show a phenological response to climate change are declining.
Proceedings of the National Academy of Sciences of the Unites States of America
105, 16195–16200.
Monahan, W.B. and Tingley, M.W. 2012. Niche tracking and rapid establishment of
distributional equilibrium in the house sparrow show potential responsiveness of
species to climate change. PLoS ONE 7: e42097.
78 Monasterio, C., Salvador, A., Iraeta, P., Diaz, J.A. 2009. The effects of thermal biology
and refuge availability on the restricted distribution of an alpine lizard. Journal of
Biogeography 36: 1673–1684.
Morrison, S. and Bolger, D. 2002. Variation in a sparrow’s reproductive success with
rainfall: food and predator-mediated processes. Oecologia 133: 315–324.
Nunez, T.A., Lawler, J.J., Mcrae, B.H., Pierce, D.J., Krosby, M.B., Kavanagh, D.M.,
Singleton, P.H., Tewksbury, J.J. 2013. Connectivity planning to address climate
change. Conservation Biology 27: 407–416.
Oliver, T., Roy, D.B., Hill, J.K., Brereton, T., Thomas, C.D. 2010. Heterogeneous
landscapes promote population stability. Ecology Letters 13: 473–484.
Patten, M., Wolfe, D., Shochat, E., Sherrod, S. 2005. Effects of microhabitat and
microclimate selection on adult survivorship of the lesser prairie-chicken. Journal of
Wildlife Management 69: 1270–1278.
Pickett, S.T.A., Cadenasso, M.L., Grove, J.M., Boone, C.G., Groffman, P.M., Irwin, E.,
Kaushal, S.S., Marshall, V., McGrath, B.P., Nilon, C.H., Pouyat, R.V., Szlavecz, K.,
Troy, A., Warren, P. 2011. Urban ecological systems: Scientific foundations and a
decade of progress. Journal of Environmental Management 92: 331–362.
Rhodes, B., O’Donnell, C., Jamieson, I. 2009. Microclimate of natural cavity nests and its
implications for a threatened secondary-cavity-nesting passerine of New Zealand, the
south island saddleback. Condor 111: 462–469.
Rudd, H., Vala, J., Schaefer, V. 2002. Importance of backyard habitat in a comprehensive
biodiversity conservation strategy: A connectivity analysis of urban green spaces.
Restoration Ecology 10: 368–375.
79 Ruokolainen, L., Abrams, P.A., McCann, K.S., Shuter, B.J. 2011. The roles of spatial
heterogeneity and adaptive movement in stabilizing (or destabilizing) simple
metacommunities. Journal of Theoretical Biology 291: 76–87.
Scherrer, D. and Koerner, C. 2010. Infra-red thermometry of alpine landscapes
challenges climatic warming projections. Global Change Biology 16: 2602–2613.
Smith-Castro, J.R. and Rodewald, A.D. 2010. Behavioral responses of nesting birds to
human disturbance along recreational trails. Journal of Field Ornithology 81: 130–
138.
Speed, C.W., Meekan, M.G., Field, I.C., McMahon, C.R., Bradshaw, C.J.A. 2012. Heatseeking sharks: support for behavioural thermoregulation in reef sharks. Marine
Ecology Progress Series 463: 231–244.
Suggitt, A.J., Gillingham, P.K., Hill, J.K., Huntley, B., Kunin, W.E., Roy, D.B., Thomas,
C.D. 2011. Habitat microclimates drive fine-scale variation in extreme temperatures.
Oikos 120: 1–8.
Taha, H. 1997. Urban climates and heat islands: Albedo, evapotranspiration, and
anthropogenic heat. Energy and Buildings 25: 99–103.
Thorington, K.K. and Bowman, R. 2003. Predation rate on artificial nests increases with
human housing density in suburban habitats. Ecography 26: 188–196.
Thuiller, W., Lavorel, S., Araujo, M., 2005. Niche properties and geographical extent as
predictors of species sensitivity to climate change. Global Ecology and Biogeography
14: 347–357.
Tieleman, B.I., van Noordwijk, H.J., Williams, J.B. 2008. Nest site selection in a hot
desert: Trade-off between microclimate and predation risk? Condor 110: 116–124.
80 Trammell, E.J. and Bassett, S. 2012. Impact of urban structure on avian diversity along
the Truckee River, USA. Urban Ecosystems 15: 993–1013.
Tremblay, M.A. and St Clair, C.C. 2011. Permeability of a heterogeneous urban
landscape to the movements of forest songbirds. Journal of Applied Ecology 48: 679–
688.
Van Heezik, Y., Ludwig, K., Whitwell, S., McLean, I.G. 2008. Nest survival of birds in
an urban environment in New Zealand. New Zealand Journal of Ecology 32: 155–
165.
Virkkala, R., Heikkinen, R.K., Fronzek, S., Kujala, H., Leikola, N. 2013. Does the
protected area network preserve bird species of conservation concern in a rapidly
changing climate? Biodiversity and Conservation 22: 459–482.
Virkkala, R., Heikkinen, R.K., Leikola, N., Luoto, M. 2008. Projected large-scale range
reductions of northern-boreal land bird species due to climate change. Biological
Conservation 141: 1343–1353.
Wiebe, K.L. and Gerstmar, H. 2010. Influence of spring temperatures and individual
traits on reproductive timing and success in a migratory woodpecker. Auk 127: 917–
925.
81 TABLE 1. Categorical threshold values of temperatures and the timing of breeding used
to analyze nest habitat use by three bird species breeding in urban habitat in northern
Nevada. Percentages of four land cover types within a 20m radius of nests and nest
height are also reported. All values are means (µ) ± SD for the associated sample size
(N).
Variable
Tmax5 (°C)
cold
mild
warm
Timing (ordinal day)
early
middle
late
20m Cover (%)
bare ground
impervious
canopy
vegetation
Nest height (m)
µ
Robins
SD
Doves
SD
N
µ
15.8
20.9
27.2
1.5
1.8
1.7
49
68
27
114
126
141
5
6
10
9
14
28
38
3.9
13
17
18
21
1.6
Grosbeaks
µ
SD
N
N
14.6
19.2
28.0
1.7
1.9
2.1
53
63
17
17.4
23.5
27.9
1.8
1.2
1.6
14
12
17
49
47
48
93
112
139
8
8
18
45
44
44
139
145
155
6
7
7
15
13
15
144
7
16
31
38
4.2
10
19
17
20
1.7
133
4
5
23
56
2.9
6
9
17
20
1.8
43
82 FIGURE 1A - F. Hypothesized effects of temperature on nest site selection. Nest habitat
on the Y axis represents % cover of one of the land cover variables measured (bare
ground, canopy cover, impervious surfaces, or understory vegetation). Relative timing of
nesting in the breeding season on the X axis represents seasonal variation of any one of
several time varying factors that have the potential to influence nest site selection (e.g.
predation risk, human activity). Lines represent different levels to air temperature at the
time of nesting (cold = dashed grey, mild = solid grey, warm = solid black).
Interpretation of the graphical illustration is provided by the presence or absence of a
significant effect. (after Gotelli and Ellison 2004)
FIGURE 2. Daily variation of maximum air temperatures during spring and summer in
Reno, Nevada from 2009 to 2012.
83 84 FIGURE 3A - D. The influence of land cover within 20m on Tmax (left) and Tmin
(right) recorded at 48 microsites in urban reserves from April 21 to July 13, 2011.
Relative levels of land cover types are categorized into three groups of low, mid, and
high. The upper percentage threshold value of low, mid, and high levels were 30, 55, and
88 for understory vegetation (A); 6, 24, and 75 for impervious surfaces (B); 10, 30, and
92 for canopy cover (C); and 8, 19, and 48 for bare ground (D).
FIGURE 3A – D, continued.
85 86 FIGURE 4A-D. Microhabitat used at 278 robin and dove nest sites for different levels of
the timing of breeding (early, middle, late) and the relative daily temperature maximum
in the five days before nesting (cold, mild, warm). Temperature levels differed by 7.5 °C
and timing levels differed by an average of 21 days. The four land cover variables at a
nest that could have varied with time or temperature were understory vegetation (A),
impervious surfaces (B), bare ground (C), and canopy cover (D).
87 FIGURE 5. Counts of corvids observed on transects at eight sites in a riparian corridor of
northern Nevada. Relative maximum temperatures on the day of a survey (cold, mild,
warm) are compared within a site over eight survey intervals. Each temperature level
spanned a 7.5 °C increment.
88 THE INFLUENCE OF URBAN CLIMATE ON TREE, INVERTEBRATE, AND
NESTING PHENOLOGY
MILES E. BECKER AND PETER J. WEISBERG
ABSTRACT
Urban development modifies the thermal environment in addition to causing structural
changes in habitat. The timing of life cycle events in many species is sensitive to
temperature variation and may shift along an urban to rural temperature gradient. Rates
of temperature-related phenological shifts are often species-specific and there is the
potential for uneven responses in timing between trophic levels in urban sites. We
studied the phenology of leaf out, invertebrate abundance, and nesting of American
Robin (Turdus migratorius) on an urban to rural gradient in northern Nevada. We
recorded air temperatures and the dates of bud burst in Salix spp. and Populus spp., peak
abundance of aerial, foliar, and aquatic guilds of invertebrates, and robin clutch initiation
at nine sites along a riparian corridor. Daily maximum temperatures were highest in the
rural sites and next highest in the urban sites. The sequence in phenology of robins,
aerial and foliar invertebrates all shifted across sites, but in different directions and at
different rates. The responses of robins and foliar invertebrates to the temperature
gradient were also associated with the timing of bud break. The association between
urban development and phenology was primarily indirect through a delay in life cycle
events at cooler sites in the suburbs. Phenological shifts in the urban landscape are
uneven between trophic levels, similar to the effects of regional climate change on
phenology. Future studies should investigate the population level consequences for a
potential phenological mismatch between predators and herbivore prey in urban habitat.
89 INTRODUCTION
Urban development can be a major factor altering the ecological processes
important for native species persistence. The primary manifestation of urban
development is the loss or degradation of natural systems by vegetation removal, paving
of topsoil, and the addition of air and water pollutants (Alberti 2005, Grimm et al. 2008).
Direct disturbance of habitat structure can have immediate impacts on local populations
of animals, like vehicle collisions on roads (Taylor and Goldingay 2009), utility line
electrocutions and window collisions (Hager 2009), or the destruction of burrows (Trulio
1995). Habitat modification associated with development may also impact native species
in less obvious ways, such as a shift in temperature regimes.
The Urban Heat Island (UHI) typically creates warmer nights, milder winters, and
hotter summers in a city than the surrounding natural habitat (Brazel et al. 2007, Imhoff
et al. 2010). The expression of the UHI is similar to some climate change forecasts but it
is strongly linked to local land cover. The replacement of vegetation by impervious
surfaces absorbs heat in the day and radiates stored heat at night (Grimmond and Oke
1999, Oke 1988). The magnitude of the UHI may be counteracted by an increase in
canopy cover and irrigated landscaping that provides daytime shade and
evapotranspiration (Cao et al. 2010, Jenerette et al. 2007, Zhou and Wang 2011). In
addition, the heterogeneity of land cover types between neighborhoods and individual
properties within the umbrella of the UHI is likely to generate fine-scale thermal
heterogeneity, which may have biological significance.
The ecological relevance of changing temperature regimes is frequently studied in
90 the context of regional, long-term climate change. There is ever increasing
documentation of a variety of subtle and dramatic responses to climate change across
multiple taxa in natural systems (Chen et al. 2011, Parmesan and Yohe 2003). One welldocumented behavioral adjustment to changing temperatures is a shift in the timing of
temperature sensitive life-cycle events such as reproduction and migration (Jonzen et al.
2006, Møller et al. 2008, Stefanescu et al. 2003). Since phenology has consequences for
fitness (Chuine 2010), understanding the relationship between phenology, temperature,
and habitat is of great value for conservation planning. Urban landscapes are especially
under-represented in the phenological literature even though they are the fastest growing
land use (Seto et al. 2012) and will be encountered by many more plant and animal
populations in the future.
Urban habitat may have consequences for a target population by altering its
phenology or the phenology of other closely associated organisms. Plant bud burst and
flowering, invertebrate emergence, and the onset of breeding in birds are all temperature
sensitive and their fitness is often interdependent. Milder cold nights during winter in
urban areas may advance the timing of green up through the acceleration of accumulated
growing degree days (Zhang et al. 2004). Invertebrate emergence and activity can be
related to temperature (Pearce-Higgins et al. 2010, Tobin et al. 2008) and may also
advance in warmer urban habitat. Warmer temperatures from local weather stations were
a good predictor of Collared Flycatcher (Ficedula albicollis) laying date (Brommer et al.
2005), and lay date was most closely associated with weather from local stations in the 5day period before laying for Northern Flickers (Colaptes auratus; Wiebe and Gerstmar
2010). The phenology of trees, invertebrates, and birds could advance in areas with
91 urban warming, though perhaps at different rates.
Other cues besides temperature may also be used to initiate phenological events.
Herbivorous invertebrates dependent on the foliage of a host plant may benefit from
timing their life cycle events to leaf out instead of temperature. Similarly, many birds use
foliage to shield nests from solar radiation or conceal nests from predators. Blue Tits
(Cyanistes caeruleus) in Corsica timed breeding to coincide with leaf out of the dominant
vegetation type, not spring temperatures (Bourgault et al. 2010). Altered plant
community composition along the urban to rural gradient may produce a gradient of
green up that is independent of temperature variation (Shustack et al. 2009). It is
therefore possible for invertebrates and birds to track leaf phenology in addition to or
instead of urban warming. Similarly, other factors unique to urban habitat, such as
increased irrigation, pesticide applications, and increased densities of breeding birds may
exert a direct influence on phenology.
Phenology could be influenced by urban development through multiple pathways
including temperatures, phenology of other taxa, or environmental drivers unique to
urban areas. Trees, invertebrate communities, and birds have been studied independently
in urban habitats to some extent, yet the relative phenological response of all three
trophic levels is unknown because they have rarely been studied in the context of a single
survey. We observed the timing of bud burst in two native tree genera, peak abundance
of foliar, aerial, and aquatic invertebrates, and nesting by American Robin (Turdus
migratorius) at sites along a gradient of urban development in northern Nevada. Many
studies have successfully demonstrated phenological shifts in response to regional
climate over long time periods (Both et al. 2006, Pereyra 2011). However, we used
92 spatial variation in temperatures within a single season rather than temporal variation
between years to examine phenological variability. We expected all taxa to advance
phenology in the more urban sites, but at different rates, and that phenological shifts
would be mainly attributed to urban climatic variability and not other urban features such
as irrigation, pesticides, or densities.
METHODS
Study Area
The study area, a series of parks on a 28.4 km transect, provided a cross-section of
urban, suburban, industrial, and rural land uses in Reno and Sparks, Nevada (Figure 1).
Rapid outward growth from the city center has led to the expansion of suburbs and
housing developments into sagebrush habitat adjacent to riparian woodlands along the
Truckee River that runs west to east through town. Much of the study area is on the
historic floodplain of the Truckee Meadows and is bounded in three directions by hills
and mountain ranges, including the eastern Sierra Nevada.
We selected eight public parks and one privately owned restoration site using the
criteria of park size, accessibility, and the presence of riparian habitat that required all
sites be located adjacent to the Truckee River. Remnant patches of riparian woodland
were dominated by cottonwood (Populus spp.) and willow (Salix spp.). The less
abundant non-native Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus
pumila) are naturalized along the river. Average study site size was 9.9 ha and
neighborhoods adjacent to the parks include apartment complexes, single family
93 residences, industrial complexes, pasture, and rangeland.
Urban metrics
Urban development can be characterized in various ways. The level of
urbanization of patches of native vegetation in a matrix of other land uses has been
described by the dominant land use in the surrounding area (Kennedy et al. 2010) or a
combination of land uses and land cover, such as agriculture to represent non-urban
habitat, road length, and mowed surfaces (Sundell-Turner and Rodewald 2008). Ryder et
al. (2010) used 30m resolution land cover data to quantify impervious surface and canopy
density within 100 – 1000m radii of nests as a measure of ecologically relevant urban
development in Washington, D.C. We used three land cover classifications – percent
impervious surface, canopy cover, and bare ground – and road density to represent an
urban to rural gradient. Land cover classifications were derived from 1m resolution
orthophotographs collected in 2010 by the National Agriculture Imagery Program (NAIP)
using a geographic object-based image analysis (GEOBIA) with an overall accuracy of
94% (AMEC 2012). Road data were publicly available from Washoe County GIS (Reno,
Nevada, USA). Road density was calculated from sum of the length of all roads within
each study site and a surrounding 100m buffer divided by the area of the study site in
hectares.
Land cover and land use percentages were calculated for a 100m buffer around
each park that did not include the park surface itself using ArcGIS software (ArcMap 10,
ESRI, Redlands, California, USA). The 100m scale represents the distance of effective
heat transfer from surrounding neighborhoods across the park boundary (Jansson et al.
94 2007, Bowler et al. 2010). The four site variables were entered into a Principal
Components Analysis (PCA) and the first two principal components explained 87% of
the variation between sites. A PCA for five land cover and land use variables had a
similar explanatory power (80%) for variation between forest stands on an urban to rural
gradient in Ohio (Shustack and Rodewald 2010).
We used a weighted average of the first two principal components scores for each
site as an index of urban development using the eigenvalues for the respective axes.
Rural sites scored negative and urban sites scored positive values. The PCA weighted
average is referred to throughout the text as the ‘urban index’ and used for all site level
analyses. The nine sites were clustered in three groups in ordination space, which we
labeled as urban, suburban, and rural zones to facilitate interpretation of site level
differences related to urbanization. Urban sites had more canopy cover and impervious
surfaces, less bare ground, and higher road densities than rural and suburban sites. Rural
sites had the most bare ground and lowest road densities. The zonal groups were used
only in the analysis of invertebrate abundance and otherwise functioned as a descriptive
tool to assist visualization of the relative urban development of a site. Our definitions of
zones based on the habitat factors used in the PCA are intended to be biologically
meaningful and they may differ from zonal definitions employed by city planners that use
other criteria for designating land uses.
Temperatures
Five to nine sampling stations were established at each site to record air
temperature, invertebrate abundance, and tree phenology in 2011. Stations within a site
95 were placed at evenly spaced intervals of approximately 150m. Thermocron iButton data
loggers (N = 52; DS1921G, Dallas Semiconductors/MAXIM, Dallas, Texas, USA) were
deployed in mid-March, programmed to record temperature every 4 hours, and retrieved
in mid-July. Data analysis included temperature measurements from March 17 after all
iButtons had time to adjust to field temperatures and ended on July 6 before they were
retrieved. All iButtons were placed out of direct sunlight under the bark on the north side
of a tree and at a height of 1.5m.
Tree Phenology
Leaf phenology in deciduous trees can be measured with a qualitative scale of bud
break and flowering that is relevant to invertebrates and songbirds (McGrath et al. 2009).
Bud break was measured in April and May 2011 at a sub-set of temperature stations that
had a Salix (N = 37) or Populus (N = 30) tree within 20m of the station. Trees sampled
for bud break had secondary growth taller than 2m. Bud break was measured by scoring
the apical bud on four branches closest to chest height in each of the cardinal directions.
The stem directly before the apical bud was marked by a dot with a permanent marker to
allow resampling the same bud on returning visits. Developmental stages of buds were
given a score of 0 – 5 following Pellis et al. (2004; Table 1). Reference photos were used
to maintain observer consistency in scoring bud break. A tree was considered to have
progressed to bud burst when the average of four apical buds on a tree had reached a
score of 4. Site level bud break date was the average date for all trees sampled within the
site.
96 Invertebrate Phenology
Several methods exist for sampling invertebrates. We used a trapping method
that allows continuous sampling over several days and temperature ranges. Pitfall traps
are capable of multi-day trapping but are biased towards ground dwelling invertebrates
(Rohr et al. 2007). Instead, we used adhesive traps that catch a combination of aerial,
foliar, and adult aquatic invertebrates. Even so, relative abundance between taxa does not
represent true relative abundance because the sampling method was biased to capture
aerial invertebrates.
Adhesive traps were yellow 7.6 x 12.7 cm cards coated in a clear adhesive (Stiky
Strip Traps, Olson Products Inc.). Traps were located at 64 stations (5-8 per site) and
hung with a short wire from a branch on the western side of the tree crown at a height of
approximately 2m. Traps were retrieved and replaced at weekly intervals from May 1 –
July 17, 2011. An index of the timing of peak abundance at a site for each taxon was
identified by the sampling date with the highest count of individual invertebrates. A
second measure of invertebrate activity, peak magnitude, was the value from the
sampling date with peak abundance. The unit of measurement in all invertebrate analyses
was number of invertebrates captured per day.
Invertebrates were identified to order or sub-order and placed into guilds of aerial,
aquatic, or foliar. Aerial insects primarily moved by flight as adults, were often captured
in air by hawking birds, and generally acquired nutrition by means other than herbivory.
Invertebrates whose larval stage is underwater in ponds or rivers were classified as
aquatic. Foliar invertebrates traveled more often by walking, were herbivorous or
strongly associated with plants (i.e. herbivore predators), and often found themselves the
97 target of foliage gleaning by foraging birds.
Nest Phenology
Robins are present year-round in the study area and begin breeding by mid-April.
Robins are foraging and nest site generalists, but despite their apparent lack of niche
specificity, they have been documented to adjust arrival at breeding habitat to spring
temperatures (Inouye et al. 2008).
Nests were located starting in March or April from 2009 to 2012 (N = 155).
Nests higher than 8m were excluded because of less accurate ageing of nests. Nest
contents were checked every three to four days with a mirror pole to age nests. Nests
were aged using laying sequence during egg laying, by counting back from hatch day, or
by ageing nestlings.
Individuals in this study were not uniquely marked, and so first nesting attempts
could not be reliably segregated from sequential nest attempts. Instead, we estimated site
level clutch initiation dates using the 25th percentile of nesting date. Other studies have
used median or mean nesting date for marked populations (Both et al. 2005, Bourgault et
al. 2010, Pereyra 2011) and the 25th percentile is a more conservative estimate.
While our predictor variables of interest were related to habitat and temperature,
clutch initiation can be influenced by the density of breeding conspecifics (Evans et al.
2009) and breeding bird density often increases at more urban sites (Rodewald and
Shustack 2008). We estimated the density of robins at a site in 2011 using distance
sampling (Buckland et al. 2001, Gottschalk and Huettmann 2011) on line transects
through each site. Observations on transects were between 0630 and 1000 with eight
98 replicates per site at five to eight day intervals. Distance sampling was used to calculate
density estimates as individuals per ha. Estimates of robins per ha in each site were
calculated in Program DISTANCE (version 6.0 Release 2, Research Unit for Wildlife
Population Assessment).
ANALYSIS
Temperature, urbanization, and phenology
We followed two analytical approaches to addressing the questions of how urban
development influences temperatures and how phenology across taxa responds to both
predictor variables. Individual relationships between sites and temperatures were first
analyzed in a univariate ANOVA for average daily temperature maximum (Tmax) in
April. Most of the phenological events occurred during May and June, and they were
most likely influenced by prior temperatures in April. Tmax was used because it is
related to the accumulation of growing degree days that influences leaf phenology and
also invertebrate emergence and nest phenology. Easting was used as a covariate in the
analysis because the sites follow a west to east transect at increasing distances from the
Sierra Nevada. Cold air masses, hydrological characteristics, or other landscape scale
thermal dynamics may change across the gradient.
The second approach was to collectively examine the relationships between urban
development, temperature, and phenology in a path analysis. Path analysis uses a series
of multiple regressions to quantify hypothesized relationships between exogenous
99 predictor variables and endogenous response variables. The main advantage of a path
analysis approach is to isolate the direct and indirect relationship of a predictor variable,
urban development, with a response variable, phenology. Model fit for individual taxa
phenologies were assessed with the coefficient of determination (R2) and the residual
error associated with the response variable. Path coefficients displayed in the results are
standardized Beta coefficients that show how much a one standard deviation change in
the predictor variable is estimated to change the response variable.
We used site level averages for the date of peak invertebrate abundance for three
guilds, and clutch initiation as the response variables and urban index, Easting, bud
break, and temperature as the predictor variables. In the models, urban index scores
could influence invertebrate and robin phenology directly or indirectly through
temperatures or date of leaf out by Populus (Figure 2). Populus phenology was used over
Salix because a priori univariate testing showed a strong relationship between robin nest
phenology and Populus bud break (Pearson’s = 0.817, P = 0.013) but not with Salix
(Pearson’s = 0.175, P = 0.824). Populus was used for the other models to maintain
consistency. Prior examination of temperature differences between sites revealed that
there was a quadratic effect of urban development on daily Tmax. We included a
quadratic term for urban index in the path analysis (Scheiner et al. 2000) that was
modeled to influence only temperature (Figure 2). Four years of nest phenology data
were sampled at the sites even though temperatures and bud break were only sampled in
2011. We included all four years of nest data for a larger sample size to better estimate
the sequence of nest phenology across sites. Phenological sequences cued by urban
development or geographic location of a site should be consistent from year-to-year since
100 neither variable changed during the study period. Breeding density of birds can influence
nest phenology, but there was almost no relationship between 25th percentile lay date and
the density of robins at a site (P = 0.525, r2 = 0.09), so robin density was also omitted
from the model.
RESULTS
Site Features
Individual sites differed in size, geography, and proportion of land cover types in
the matrix outside their boundaries (Table 3). The urban gradient captured by the Urban
Index represented a shift from rural sites with lower road densities and more bare ground
to urban sites with more canopy cover and impervious surfaces. Impervious surface
changed substantially between sites, up to 45%, while canopy cover changed the least
amount. The sites were located on a west to east transect where Easting was highly
correlated with elevation (Pearson’s = -0.982, P <0.001, N = 9). The most western site
was 110m higher in elevation than the most eastern site, for a slope of 0.4% over the
entire study area.
Urban influence on Temperatures
Maximum daily temperatures in April differed between sites by up to 4.6°C and
by 5°C in June. Temperature differed significantly between sites in April (F6,289 = 4.003,
P < 0.001) and June (F7,379 = 3.467, P = 0.001). Tmax was highest in the rural sites in
both April and June (Figure 3). The most urban site had the next highest Tmax in April
101 and, on average, suburban sites had the lowest observed Tmax in April (Figure 3).
Phenology
Salix were the first to reach their phenological event across all individuals in the
study population (4 April ± 10 days), followed by Populus bud break (1 May ± 7 days),
nesting by robins (1 May ± 5 days), peak abundance of foliar (31 May ± 24 days), aerial
(15 June ± 20 days), and aquatic invertebrates (26 June ± 18 days). The intensity of the
shift in phenology between sites differed between taxa (Figure 4). Salix bud break
advanced by up to 14 days between sites and Populus by up to 16 days. Robins nested up
to 11 days earlier at some sites on the 28.4 km transect. Changes in the timing of peak
invertebrate abundance were the most dramatic: up to 48 days for foliar invertebrates and
up to 55 days for aerial invertebrates.
The sequence of phenology for trees, insects, and birds also differed across sites
(Figure 4). The sequence of bud break by Populus paralleled the site level temperatures
with earlier phenology at warmer sites. Path analysis supported a strong negative
association between temperature and bud break and a positive association between
easting and bud break (Figure 5). The timing of nesting by robins at sites was similar to
bud break by Populus (Figure 4) and the two phenologies were closely associated in the
path analysis (Figure 5). Bud break was somewhat associated with peak abundance of
foliar invertebrates (Figure 5) but the sequence of invertebrate peak abundances was
fairly idiosyncratic for all three guilds (Figure 4). The path model was not an equally
good fit for all the taxa. Robin clutch initiation was closest (R2 = 0.981) and aerial and
foliar invertebrates were reasonable (aerial: R2 = 0.701; foliar: R2 = 0.764). The paths for
102 Populus bud break were a slightly better fit than invertebrates (R2 = 0.893). The model
of site level temperatures in April also fit the data moderately well when explained by the
urban index at sites (R2 = 0.746).
More aerial invertebrates were captured than the two other guilds, although aerial
invertebrates had the fewest number of orders, sub-orders, or families (Table 4). Too few
aquatic invertebrates were captured to be able to demonstrate any pattern (Figure 6). The
lowest observed abundances of foliar and aerial invertebrates occurred in the urban sites
(Figure 6), but did not significantly differ between zoning groups (foliar: F3,8 = 4.23, P =
0.099; aerial: F3,8 = 0.49, P = 0.732).
DISCUSSION
Phenology has the potential to shift along an urban to rural gradient in response to
temperatures, biological interactions, or other factors present in the urban environment.
We found that the sequence of timing of bud break, peak invertebrate abundance, and
nesting by robins differed between sites on an urban to rural gradient. The indirect effect
of the surrounding matrix on site level temperatures was associated with advancing
phenology of Populus, robins, and aerial invertebrates. Robin nest phenology and peak
foliar abundance was also associated with leaf phenology. Urban sites tended to have the
lowest abundances of invertebrates.
Urban Development and Temperatures
103 The relationship between temperature and urban development in the study area
was not typical of the Urban Heat Island effect described for cities in temperate and
tropical regions. Usually, temperatures within the city boundaries are warmer than
adjacent land used for other purposes. Rural sites in this study reached the warmest
temperatures in spring and summer, most likely because of a limited 3-5 % canopy cover
and abundant bare ground or low growing sparse vegetation characteristic of sagebrush
communities in the western Great Basin. Solar radiation rapidly heats surfaces in the
absence of shade (Suggitt et al. 2011, Vargo et al. 2013), promoting warmer temperatures
at rural sites. The subrural, suburban, and urban sites were surrounded by 2 to 3 times
more canopy cover, which could effectively cool air by shading or evapotranspiration
(Cao et al. 2010). The urban sites were the next warmest, with the coolest spring
temperatures generally in the suburban sites. This “U-shaped” spatial temperature trend
has been described as uniquely characteristic to cities in arid and desert regions, where
the Urban Heat Island is less pronounced (Imhoff et al. 2010). The relationship between
urban development in the matrix surrounding parks and site temperatures may have been
even greater if our study sites were located farther from the river because water can
dampen temperature amplitudes.
Phenology
Temperature variation between sites was strongly associated with differences in
the sequence of phenological events. The relationship between tree leaf out and growing
degree days is well established (Bronson et al. 2009, Richardson et al. 2006) and it was
expected that Populus would leaf out earlier at sites with higher Tmax, representing faster
104 accumulation of growing degree days. Populus bud break did occur earlier at warmer
sites, but Salix did not follow the same pattern. Salix may have greater sensitivity to
other urban factors, like irrigation levels, nutrient cycling and soil biochemistry, or soil
compaction and composition. There are seven Salix species, including at least one
naturalized species, and only two Populus species in the Truckee Meadows (pers. comm.
Otis Bay Ecological Consultants, Mogul, Nevada). Genetic variation within a single
genus can lead to variation in leaf out under the same environmental conditions (Pellis et
al. 2004). Salix composition may have differed between sites and masked any unified
response to temperature.
The timing of bud break by Populus was also strongly associated with the
position of sites on a West to East gradient. Temperatures and tree species composition
could have changed along the geographic gradient, which may have accounted for the
significant relationship. Easting and Tmax were related in the path model, suggesting
that temperatures may have been influenced by geography instead of urban development.
However, the variation in temperatures between sites did not follow a linear trend with
Easting, allowing for another factor, likely urban development, to contribute to
temperature differences between sites.
Robins varied the timing of clutch initiation between sites by up to 11 days. The
phenological shift was partly associated with the direct influence of temperatures at a site,
but the relationship with leaf phenology of Populus was stronger. Temperature can be a
secondary cue, after photoperiod, for birds to initiate nesting (Brommer et al. 2005,
Coppack and Pulido 2004). Birds may also use leaf out as a cue to begin breeding. Great
tits and blue tits initiated nesting in association with the timing of leaf out in different
105 habitats, not temperatures (Nilsson and Kallander 2006). Previous studies have found
that other bird species synchronize the timing of nesting with the timing of foliar
invertebrates, the preferred prey item in their nestling diets (Both et al. 2006, Burger et al.
2012).
Closely synchronized phenology between robins and Populus in this study likely
had little to do with food resources. Robins generally eat soil macroinvertebrates that are
not associated with plant herbivory and the functional relationship between robins and
trees is not linked by food. Instead, robins use trees primarily for nesting and the strong
relationship between leaf out of Populus and not Salix suggests the former is more widely
used by nesting robins. Indeed, over four years of observing robin nests, 8% were
constructed in Salix and 43% in Populus even though the relative availability of each tree
species was almost exactly one to one in the study area (unpublished data). Foliage
provides nest concealment from predators (Chalfoun and Martin 2009) and some species
select nest sites with more cover across spatially heterogeneous habitat (Chalfoun and
Martin 2010, Kolada et al. 2009). Our results suggest that temporal variation in the
availability of less risky nest sites may drive the timing of clutch initiation. The early
timing of leaf out in the exotic shrub Lonicera promoted early nesting by Northern
Cardinals, even though those nests were more likely to fail (Rodewald et al. 2010). The
benefit of waiting for Populus to leaf out could be tested in future studies by comparing
nest survival of nests constructed in trees before and after leaf out.
Phenology of the invertebrate guilds responded differently to urban development,
temperatures, and Populus leaf phenology. Peak abundance in aerial invertebrates was
the most sensitive to the urban index and Tmax at a site, peaking earliest at the most rural
106 site that was warmest. Invertebrate abundance of foliar arthropods on potted plants in
Phoenix, Arizona also peaked earlier at rural sites (Bang et al. 2012). Foliar insects in
our study were expected to peak earlier at sites where their host plants also leaf out earlier
(Mussey and Potter 1997). In this case, foliar invertebrates were expected to peak earlier
at sites where Populus peaked earlier. The path analysis showed foliar invertebrates had
the strongest association with bud break of the three guilds. However, foliar
invertebrates in this study did not peak earlier in both the warmer urban and rural sites,
perhaps because of the phenology of other plants besides Populus. Native plant richness
may decrease in urban areas (Godefroid and Koedam 2007) where there may be fewer
resources or narrower windows of leaf and flower phenology for foliar invertebrates.
Urban areas had the lowest abundance of aerial and foliar invertebrates, perhaps from
limited food resources. Further research on separating out the temperature effects from
plant species richness effects on invertebrate phenology would be useful.
Not surprisingly, there was no influence of any of the three terrestrial metrics
sampled on the phenology of aquatic invertebrates. The sample size for that guild was
relatively low compared to the other two guilds because adult aquatic invertebrates were
mainly bycatch in the trapping method that was biased towards terrestrial invertebrates.
Even so, the 1262 aquatic invertebrates collected across nine sites showed that the site
level factors we measured were not significantly related to their phenology. However,
aquatic invertebrates can be an important part of nutrient cycling between aquatic and
terrestrial food webs (Nakano and Murakami 2001) and the relative timing of their peak
abundances can respond to different factors (Dineen et al. 2007). Urbanization can affect
aquatic systems from the addition of nutrients and chemicals into urban streams and
107 rivers or channelization (Nilsson et al. 2003), but at the scale of the entire city or
watershed and not at individual parks that we sampled. Temperatures can also affect the
timing of aquatic invertebrate emergence (Harper and Peckarsky 2006), but water
temperatures in the river at a single park are likely more influenced by stream
morphology, snow-runoff, and upstream factors that we did not sample.
Urban warming and Phenology
We found an indirect association of suburban cooling on phenology, but
phenology was mediated by interactions with other species, other urban factors, and
geography. The taxa spanning three trophic levels in this study differed in the magnitude
of their response to the same predictor variable and some were more sensitive than others
to different predictor variables. Even though there was not a direct link between all three
trophic levels, changes in the sequence of phenology across the rural to urban gradient
could result in a phenological mismatch similar to observations of natural systems
undergoing long-term regional climate change. Lay date in some urban birds may be
constrained by the availability of canopy cover at nest sites, a potential trade-off with
synchronizing nesting to invertebrate prey availability. For example, shorter and lower
magnitude peaks in invertebrate prey in urban habitat may make it less productive for
some foraging guilds. Acadian Flycatchers that primarily capture aerial insects nested
later and had higher turnover in urban forests, suggesting that it was marginal habitat
(Shustack and Rodewald 2010). Species richness and abundance of insectivorous birds
often decline in urban habitat (Dunford and Freemark 2005, Schlesinger et al. 2008),
perhaps for the same reason.
108 Conclusions
Urban development modifies the thermal environment in ways that may affect
biological processes. Phenology is an important factor in fitness and can have
consequences for population dynamics (Chuine 2010). Phenology is known to shift in
response to climate change (Chen et al. 2011, Parmesan and Yohe 2003) and also across
habitats and land uses (Altermatt 2012, Burger et al. 2012). Since the rates of
phenological shifts are often species-specific (Donnelly et al. 2011), predicting
community level responses to global change requires simultaneous observation of
multiple species (Ibanez et al. 2010). This is the first study to our knowledge that follows
the phenology of three trophic levels across the urban to rural gradient. While other
major features of urban development, such as habitat fragmentation or habitat loss, may
impact plant, invertebrate, and bird populations, the results of this study suggest indirect
effects of temperature variation can also influence life cycles. The mistiming of
phenology between trophic levels can have serious implications for population viability
of a single species (Visser 1998) or multiple species interactions (Yang and Rudolf
2010). Whether or not phenological shifts in the urban landscape are sufficient to affect
population dynamics remains an open question for future studies.
109 LITERATURE CITED
Alberti, M. 2005. The effects of urban patterns on ecosystem function. International
Regional Science Review 28: 168–192.
Altermatt, F. 2012. Temperature-related shifts in butterfly phenology depend on the
habitat. Global Change Biology 18: 2429–2438.
AMEC Environment and Infrastructure. 2012. Urban tree canopy assessment Truckee
Meadows (Reno-Sparks, Nevada). 53pp.
Bang, C., Faeth, S.H., Sabo, J.L. 2012. Control of arthropod abundance, richness, and
composition in a heterogeneous desert city. Ecological Monographs 82: 85–100.
Both, C., Bijlsma, R.G., Visser, M.E. 2005. Climatic effects on timing of spring
migration and breeding in a long-distance migrant, the pied flycatcher Ficedula
hypoleuca. Journal of Avian Biology 36: 368–373.
Both, C., Bouwhuis, S., Lessells, C.M., Visser, M.E. 2006. Climate change and
population declines in a long-distance migratory bird. Nature 441: 81–83.
Bourgault, P., Thomas, D., Perret, P., Blondel, J. 2010. Spring vegetation phenology is a
robust predictor of breeding date across broad landscapes: a multi-site approach
using the Corsican blue tit (Cyanistes caeruleus). Oecologia 162: 885–892.
Bowler, D.E., Buyung-Ali, L., Knight, T.M., Pullin, A.S. 2010. Urban greening to cool
towns and cities: A systematic review of the empirical evidence. Landscape and
Urban Planning 97: 147–155.
Brazel, A., Gober, P., Lee, S.-J., Grossman-Clarke, S., Zehnder, J., Hedquist, B.,
Comparri, E. 2007. Determinants of changes in the regional urban heat island in
110 metropolitan Phoenix (Arizona, USA) between 1990 and 2004. Climate Research
33: 171–182.
Brommer, J.E., Merila, J., Sheldon, B.C., Gustafsson, L. 2005. Natural selection and
genetic variation for reproductive reaction norms in a wild bird population.
Evolution 59: 1362–1371.
Bronson, D.R., Gower, S.T., Tanner, M., Van Herk, I. 2009. Effect of ecosystem
warming on boreal black spruce bud burst and shoot growth. Global Change Biology
15: 1534–1543.
Buckland, S.T. 2001. Introduction to distance sampling: estimating abundance of
biological populations. Oxford University Press, 432pp.
Burger, C., Belskii, E., Eeva, T., Laaksonen, T., Maegi, M., Maend, R., Qvarnstrom, A.,
Slagsvold, T., Veen, T., Visser, M.E., Wiebe, K.L., Wiley, C., Wright, J., Both, C.
2012. Climate change, breeding date and nestling diet: how temperature
differentially affects seasonal changes in pied flycatcher diet depending on habitat
variation. Journal of Animal Ecology 81: 926–936.
Cao, X., Onishi, A., Chen, J., Imura, H. 2010. Quantifying the cool island intensity of
urban parks using ASTER and IKONOS data. Landscape and Urban Planning 96:
224–231.
Chalfoun, A.D. and Martin, T.E. 2009. Habitat structure mediates predation risk for
sedentary prey: experimental tests of alternative hypotheses. Journal of Animal
Ecology 78: 497–503.
111 Chalfoun, A.D. and Martin, T.E. 2010. Facultative nest patch shifts in response to nest
predation risk in the Brewer’s sparrow: a “win-stay, lose-switch” strategy?
Oecologia 163: 885–892.
Chen, I.-C., Hill, J.K., Ohlemueller, R., Roy, D.B., Thomas, C.D. 2011. Rapid range
shifts of species associated with high levels of climate warming. Science 333: 1024–
1026.
Chuine, I. 2010. Why does phenology drive species distribution? Philosophical
Transactions of the Royal Society B: Biological Sciences 365: 3149 –3160.
Coppack, T. and Pulido, F. 2004. Photoperiodic response and the adaptability of avian
life cycles to environmental change, in: Møller, A.P., Fielder, W., Berthold, P. (eds.).
Birds and Climate Change, Advances in Ecological Research. pp. 131–150.
Dineen, G., Harrison, S.S.C., Giller, P.S. 2007. Seasonal analysis of aquatic and
terrestrial invertebrate supply to streams with grassland and deciduous riparian
vegetation. Biology and Environment – Proceedings of the Royal Irish Academy
107B: 167–182.
Donnelly, A., Caffarra, A., O’Neill, B.F. 2011. A review of climate-driven mismatches
between interdependent phenophases in terrestrial and aquatic ecosystems.
International Journal of Biometeorology 55: 805–817.
Dunford, W. and Freemark, K. 2005. Matrix matters: Effects of surrounding land uses on
forest birds near Ottawa, Canada. Landscape Ecology 20: 497–511.
Evans, L.E., Ardia, D.R., Flux, J.E.C. 2009. Breeding synchrony through social
stimulation in a spatially segregated population of European starlings. Animal
Behaviour 78: 671–675.
112 Godefroid, S. and Koedam, N. 2007. Urban plant species patterns are highly driven by
density and function of built-up areas. Landscape Ecology 22: 1227–1239.
Gottschalk, T.K. and Huettmann, F. 2011. Comparison of distance sampling and territory
mapping methods for birds in four different habitats. Journal of Ornithology 152:
421–429.
Grimm, N.B., Foster, D., Groffman, P., Grove, J.M., Hopkinson, C.S., Nadelhoffer, K.J.,
Pataki, D.E., Peters, D.P.C. 2008. The changing landscape: ecosystem responses to
urbanization and pollution across climatic and societal gradients. Frontiers in
Ecology and the Environment 6: 264–272.
Grimmond, C.S.B. and Oke, T.R. 1999. Heat storage in urban areas: Local-scale
observations and evaluation of a simple model. Journal of Applied Meteorology 38:
922–940.
Hager, S.B. 2009. Human-related threats to urban raptors. Journal of Raptor Research 43:
210–226.
Harper, M. and Peckarsky, B. 2006. Emergence cues of a mayfly in a high-altitude
stream ecosystem: Potential response to climate change. Ecological Applications 16:
612–621.
Ibanez, I., Primack, R.B., Miller-Rushing, A.J., Ellwood, E., Higuchi, H., Lee, S.D.,
Kobori, H., Silander, J.A. 2010. Forecasting phenology under global warming.
Philosophical Transactions of the Royal Society B: Biological Sciences 365: 3247 –
3260.
113 Imhoff, M.L., Zhang, P., Wolfe, R.E., Bounoua, L. 2010. Remote sensing of the urban
heat island effect across biomes in the continental USA. Remote Sensing of
Environment 114: 504–513.
Inouye, D.W., Barr, B., Armitage, K.B., Inouye, B.D. 2000. Climate change is affecting
altitudinal migrants and hibernating species. Proceedings of the National Academy
of Sciences of the United States of America 97: 1630–1633.
Jansson, C., Jansson, P.-E., Gustafsson, D. 2007. Near surface climate in an urban
vegetated park and its surroundings. Theoretical and Applied Climatology 89: 185–
193.
Jenerette, G.D., Harlan, S.L., Brazel, A., Jones, N., Larsen, L., Stefanov, W.L. 2007.
Regional relationships between surface temperature, vegetation, and human
settlement in a rapidly urbanizing ecosystem. Landscape Ecology 22: 353–365.
Jonzen, N., Linden, A., Ergon, T., Knudsen, E., Vik, J.O., Rubolini, D., Piacentini, D.,
Brinch, C., Spina, F., Karlsson, L., Stervander, M., Andersson, A., Waldenstrom, J.,
Lehikoinen, A., Edvardsen, E., Solvang, R., Stenseth, N.C. 2006. Rapid advance of
spring arrival dates in long-distance migratory birds. Science 312: 1959–1961.
Kennedy, C.M. and Marra, P.P. 2010. Matrix mediates avian movements in tropical
forested landscapes: Inference from experimental translocations. Biological
Conservation 143: 2136–2145.
Kolada, E.J., Sedinger, J.S., Casazza, M.L. 2009. Nest site selection by Greater SageGrouse in Mono County, California. Journal of Wildlife Management 73: 1333–
1340.
114 McGrath, L.J., van Riper, III, C., Fontaine, J.J. 2009. Flower power: tree flowering
phenology as a settlement cue for migrating birds. Journal of Animal Ecology 78:
22–30.
Meineke, E.K., Dunn, R.R., Sexton, J.O., Frank, S.D. 2013. Urban warming drives insect
pest abundance on street trees. PLoS ONE 8: e59687.
Møller, A.P., Rubolini, D., Lehikoinen, E. 2008. Populations of migratory bird species
that did not show a phenological response to climate change are declining.
Proceedings of the National Academy of Sciences of the United States of America
105: 16195–16200.
Mussey, G. and Potter, D. 1997. Phenological correlations between flowering plants and
activity of urban landscape pests in Kentucky. Journal of Economic Entomology 90:
1615–1627.
Nakano, S. and Murakami, M. 2001. Reciprocal subsidies: Dynamic interdependence
between terrestrial and aquatic food webs. Proceedings of the National Academy of
Sciences of the United States of America 98: 166–170.
Nilsson, C., Pizzuto, J., Moglen, G., Palmer, M., Stanley, E., Bockstael, N., Thompson,
L. 2003. Ecological forecasting and the urbanization of stream ecosystems:
Challenges for economists, hydrologists, geomorphologists, and ecologists.
Ecosystems 6: 659–674.
Nilsson, J.A. and Kallander, H. 2006. Leafing phenology and timing of egg laying in
great tits Parus major and blue tits P-caeruleus. Journal of Avian Biology 37: 357–
363.
Oke, T. 1988. The urban energy-balance. Progress in Physical Geography 12: 471–508.
115 Parmesan, C. and Yohe, G. 2003. A globally coherent fingerprint of climate change
impacts across natural systems. Nature 421: 37–42.
Pearce-Higgins, J.W., Dennis, P., Whittingham, M.J., Yalden, D.W. 2010. Impacts of
climate on prey abundance account for fluctuations in a population of a northern
wader at the southern edge of its range. Global Change Biology 16: 12–23.
Pellis, A., Laureysens, I., Ceulemans, R. 2004. Genetic variation of the bud and leaf
phenology of seventeen poplar clones in a short rotation coppice culture. Plant
Biology 6: 38–46.
Pereyra, M.E. 2011. Effects of snow-related environmental variation on breeding
schedules and productivity of a high-altitutde population of dusky flycatchers
(Empidonax oberholseri). Auk 128: 746–758.
Richardson, A., Bailey, A., Denny, E., Martin, C., O’Keefe, J. 2006. Phenology of a
northern hardwood forest canopy. Global Change Biology 12: 1174–1188.
Rodewald, A.D. and Shustack, D.P. 2008. Consumer resource matching in urbanizing
landscapes: Are synanthropic species over-matching? Ecology 89: 515–521.
Rodewald, A.D., Shustack, D.P., Hitchcock, L.E. 2010. Exotic shrubs as ephemeral
ecological traps for nesting birds. Biological Invasions 12: 33–39.
Rohr, J.R., Mahan, C.G., Kim, K.C. 2007. Developing a monitoring program for
invertebrates: Guidelines and a case study. Conservation Biology 21: 422–433.
Ryder, T.B., Reitsma, R., Evans, B., Marra, P.P. 2010. Quantifying avian nest survival
along an urbanization gradient using citizen- and scientist-generated data. Ecological
Applications 20: 419–426.
116 Schlesinger, M.D., Manley, P.N., Holyoak, M. 2008. Distinguishing stressors acting on
land bird communities in an urbanizing environment. Ecology 89: 2302–2314.
Seto, K.C., Gueneralp, B., Hutyra, L.R. 2012. Global forecasts of urban expansion to
2030 and direct impacts on biodiversity and carbon pools. Proceedings of the
National Academy of Sciences of the United States of America 109: 16083–16088.
Shustack, D.P. and Rodewald, A.D. 2010. Attenuated nesting season of the Acadian
Flycatcher (Empidonax virescens) in urban forests. Auk 127: 421–429.
Shustack, D.P., Rodewald, A.D., Waite, T.A. 2009. Springtime in the city: exotic shrubs
promote earlier greenup in urban forests. Biological Invasions 11: 1357–1371.
Stefanescu, C., Penuelas, J., Filella, I. 2003. Effects of climatic change on the phenology
of butterflies in the northwest Mediterranean Basin. Global Change Biology 9:
1494–1506.
Suggitt, A.J., Gillingham, P.K., Hill, J.K., Huntley, B., Kunin, W.E., Roy, D.B., Thomas,
C.D. 2011. Habitat microclimates drive fine-scale variation in extreme temperatures.
Oikos 120: 1–8.
Sundell-Turner, N.M. and Rodewald, A.D. 2008. A comparison of landscape metrics for
conservation planning. Landscape and Urban Planning 86: 219–225.
Taylor, B.D. and Goldingay, R.L. 2009. Can road-crossing structures improve population
viability of an urban gliding mammal? Ecology and Society 14.
Tobin, P.C., Nagarkatti, S., Loeb, G., Saunders, M.C. 2008. Historical and projected
interactions between climate change and insect voltinism in a multivoltine species.
Global Change Biology 14: 951–957.
117 Trulio, L. 1995. Passive relocation – a method to preserve burrowing owls on disturbed
sites. Journal of Field Ornithology 66: 99–106.
Vargo, J., Habeeb, D., Stone, Jr., B. 2013. The importance of land cover change across
urban-rural typologies for climate modeling. Journal of Environmental Management
114: 243–252.
Visser, M.E., van Noordwijk, A.J., Tinbergen, J.M., Lessells, C.M. 1998. Warmer
springs lead to mistimed reproduction in great tits (Parus major). Proceedings of the
Royal Society of London Series B-Biological Sciences 265: 1867–1870.
Wiebe, K.L. and Gerstmar, H. 2010. Influence of spring temperatures and individual
traits on reproductive timing and success in a migratory woodpecker. Auk 127: 917–
925.
Yang, L.H. and Rudolf, V.H.W. 2010. Phenology, ontogeny and the effects of climate
change on the timing of species interactions. Ecology Letters 13: 1–10.
Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Schneider, A. 2004. The
footprint of urban climates on vegetation phenology. Geophysical Research Letters
31.
Zhou, X. and Wang, Y.-C. 2011. Dynamics of land surface temperature in response to
land-use/cover change. Geographical Research 49: 23–36.
118 TABLE 1. Qualitative indicators of sequential stages of bud break used to measure leaf
phenology in Salix spp. and Populus spp. at nine sites on an urban to rural gradient.
Pellis Score
0
Indicator
dormant bud, no sign of any activity, completely dry
1
buds slightly swollen, color starting to change on bud scales
2
buds fully swollen and rounded, no sign of breakage of buds
3
buds started breaking, tips of shoots visible
4
bud burst and shoots green and very young leaves visible
5
green leaves growing and leaf venation visible
119 TABLE 2. Site level differences in environmental factors at nine sites on an urban to
rural gradient. The urban index is a PCA score derived from canopy cover, impervious
surface, bare ground, and road density. Estimates of relative robin densities are in the
unit of birds per hectare.
120 TABLE 3. Abundance by taxon of invertebrates captured with adhesive traps from May
to July 2011 at nine sites in northern Nevada.
Guild
Aerial
Taxon
Diptera
Hymenoptera
Lepidoptera
Guild total
Total Abundance
64320
4328
2
68650
Aquatic
Ephemeroptera
Neuroptera
Odonota
Plecoptera
Raphidiidae
Trichoptera
Guild total
108
449
3
53
90
559
1262
Foliar
Hemiptera
Arachnid
Chrysopidae
Cicadellidae
Coleoptera
Formicidae
Larvae-unknown
Guild total
4963
114
57
2691
2062
87
3
9977
121 FIGURE 1. Location of nine study sites (black) along the Truckee River (black line) on
an urban to rural gradient across the cities of Reno and Sparks, Nevada.
FIGURE 2. Hypothesized pathways between urban development and phenology.
Predictor variables are labeled in boxes and response variables are open text. Arrows
indicate the direction of the relationship between predictor and response variable.
122 123 FIGURE 3. Average daily temperature maximum at study sites in April and June 2011.
Sites are arranged left to right according to their respective level of urban development.
124 FIGURE 4. The timing of bud burst in Populus sp. and Salix sp., peak abundance for
aerial, aquatic, and foliar invertebrates, and clutch initiation in American Robin at sites
across an urban to rural gradient. Sites are arranged left to right according to their
respective level of urban development.
125 FIGURE 5. Path analysis modeling hypothesized relationships between predictor
variables (in boxes) and the phenology of leaf out, peak invertebrate abundance, and
robin lay date. The relative strength of associations between variables are indicated by
line weight, based on standardized Beta coefficients. The residual variance (error) for
each response variable are given in small boxes. Darker lines indicate a stronger
relationship and dashed lines represent a negative relationship.
126 FIGURE 6. Maximum abundance of invertebrates reached at the timing of peak
abundance in sites on a rural to urban gradient. Invertebrate guilds were aerial, foliar, or
aquatic.