Content uploaded by Ruth Kamrowski
Author content
All content in this area was uploaded by Ruth Kamrowski on Jul 03, 2014
Content may be subject to copyright.
ENDANGERED SPECIES RESEARCH
Endang Species Res
Vol. 19: 85– 98, 2012
doi: 10.3354/esr00462 Published online November 27
INTRODUCTION
The human population on Earth is expanding rap-
idly (Steck et al. 2010), and artificial light is funda-
mental to the functioning of modern society. Yet
while humans have become accustomed to artificial
cycles of light and dark, other species, especially
nocturnal or crepuscular organisms, may depend
upon natural light cycles for successful functioning
(Kramer & Birney 2001). As a result, the amount of
artificial light now used around the world is causing
some concern among scientists and conservationists
(Rich & Longcore 2006). On a global scale, the growth
of human populations in coastal zones is occurring
faster than human population growth in general
(Nicholls 1995). Due to this disproportionate growth,
coastal habitats have become some of those most vul-
nerable to light pollution (Bird et al. 2004).
Marine turtles are arguably the best-known exam-
ple of an organism adversely affected by coastal
lighting (Witherington & Martin 2000, Salmon 2003).
Dependence upon visual brightness cues for ‘sea-
© Inter-Research 2012 · www.int-res.com*Email: ruth.kamrowski@my.jcu.edu.au
Coastal light pollution and marine turtles:
assessing the magnitude of the problem
Ruth L. Kamrowski1,*, Col Limpus2, James Moloney1, Mark Hamann1
1School of Earth and Environmental Sciences, James Cook University, Townsville, Queensland 4811, Australia
2Department of Environment and Heritage Protection, PO Box 2454, Brisbane, Queensland 4001, Australia
ABSTRACT: Globally significant numbers of marine turtles nest on Australian beaches; however,
the human population of Australia is also heavily concentrated around coastal areas. Coastal
development brings with it increases in artificial light. Since turtles are vulnerable to disorienta-
tion from artificial light adjacent to nesting areas, the mitigation of disruption caused by light pol-
lution has become an important component of marine turtle conservation strategies in Australia.
However, marine turtles are faced with a multitude of anthropogenic threats and managers need
to prioritise impacts to ensure limited conservation resources can result in adequate protection of
turtles. Knowledge of the extent to which nesting areas may be vulnerable to light pollution is
essential to guide management strategies. We use geographical information system analysis to
over-lay turtle nesting data onto night-time lights data produced by the NOAA National Geophys-
ical Data Center, to assess the proportion of marine turtles in Australia potentially at risk from light
pollution. We also identify the Australian nesting sites which may face the greatest threat from
artificial light. Our assessment indicates that the majority of nesting turtles appear to be at low
risk, but population management units in Western Australia and Queensland are vulnerable to
light pollution. The risk to turtles from light generated by industrial developments appears sig -
nificantly higher than at any other location. Consequently, managers of turtle management units
in regions of proposed or on-going industrial development should anticipate potentially disrupted
turtle behaviour due to light pollution. Our methodology will be useful to managers of turtles
elsewhere.
KEY WORDS: Artificial light · Orientation · Coastal development · GIS analysis · Vulnerability
assessment
Resale or republication not permitted without written consent of the publisher
O
PEN
PEN
A
CCESS
CCESS
Endang Species Res 19: 85– 98, 2012
finding’, means the orientation of hatchling marine
turtles is disrupted by artificial lighting close to the
nesting beach (Witherington & Martin 2000, Tuxbury
& Salmon 2005). This can have serious negative con-
sequences for hatchling survival. Protracted periods
spent crawling on the beach increase predation risk,
as well as wasting the limited energy stores hatch-
lings possess from their yolk, which are necessary for
crucial offshore migration (Salmon 2006, Hamann et
al. 2007, Booth & Evans 2011).
Coastal lighting has also been reported to discour-
age adult females from nesting on particular stretches
of beach (Salmon et al. 2000). Many marine turtle
nesting beaches are located adjacent to human pop-
ulations, or to areas earmarked for development. As
human population centres expand and light levels in
coastal regions around the world increase, the avail-
ability of naturally dark beaches attractive to nesting
females is likely to decrease. This may lead to higher
concentrations of nests on beaches deemed dark
enough for nesting purposes (Salmon 2006). How-
ever, beaches with higher density nesting face a
greater likelihood of nest destruction by other nest-
ing females (Bustard & Tognetti 1969) and potentially
increased hatchling predation (Pilcher et al. 2000,
Wyneken et al. 2000). In addition, shifts in nesting
distribution may take hatchlings away from the
oceanographic features which are most favourable
for dispersal (Putman et al. 2010, Hamann et al.
2011).
Most studies concentrating on disruption to mar-
ine turtles as a result of artificial lights have been
beach specific or limited to one region (e.g. Wither-
ington 1991, Peters & Verhoeven 1994, Salmon et
al. 2000, Salmon 2003, Bertolotti & Salmon 2005,
Pendoley 2005, Stapput & Wiltschko 2005, Hare-
wood & Horrocks 2008). However, the extent of
artificial light usage is visible from space. Global
measurements of artificial light have been collected
as part of the US Air Force Defense Meteorological
Satellite Program (DMSP) Operational Linescan
System (OLS) since 1992 (Elvidge et al. 2007).
These data are freely available from the NOAA’s
National Geophysical Data Center (NGDC), and
consist of cloud-free composites created from multi-
ple nightly orbits by the DMSP satellites each year
(Elvidge et al. 1997, 2001). The DMSP images have
been em ployed for a diverse range of studies in
recent years (e.g. Aubrecht et al. 2008, Nagatani
2010, Badarinath et al. 2011), yet few studies have
utilised these global datasets with reference to
nesting turtles (but see Ziskin et al. 2008 and
Salmon et al. 2000).
The wavelengths recorded by the OLS sensor are
consistent with wavelengths disruptive to adult and
hatchling marine turtles. Both adult and hatchling
turtles have been shown to be responsive to wave-
lengths within the 440 to 700 nm range, with greatest
sensitivity at longer wavelengths (approximately
580 nm) for adults (Levenson et al. 2004) and from
350 to 540 nm for hatchlings (Witherington & Bjorn-
dal 1991, Witherington & Martin 2000, Horch et al.
2008). The OLS possesses a broad spectral response
from 440 to 940 nm, making these datasets a poten-
tially useful tool for the assessment of light pollution
impacts on turtle nesting sites (Magyar 2008).
The Australian coastline supports large and glob-
ally important marine turtle nesting aggregations
(Limpus 2009). However, >80% of Australia’s inhab-
itants live in coastal areas (Hennessy et al. 2007), and
most of the current population growth, ex cluding
capital cities, is occurring in coastal regions (Luck
2007). Currently, most beaches in northern Australia
used by nesting turtles do not experience the same
levels of human encroachment (and the associated
impacts from light pollution) that have occurred in
many other parts of the world (Chatto & Baker 2008,
Limpus 2009). However, coastal development in
northern Australia is increasing. For ex ample, the
south-east portion of Queensland (QLD) and north
Western Australia (WA), both of which support nest-
ing by multiple turtle species (Limpus 2009), are each
experiencing rapid urban growth and in dustrial
development (SEQ Catchments 2010, Australian
Bureau of Statistics 2012).
In Australia, all marine turtles are protected under
the Australian and State Governments’ conservation
legislation (Limpus 2009), and the disruptive influ-
ence of light pollution is widely acknowledged (e.g.
Department of Environment and Con servation 2007,
2008). Management actions considered necessary to
address this issue include the identification of priority
areas affected by artificial light. Yet, implementing
management strategies can be expensive and time
intensive (Fuentes et al. 2009). Knowledge of areas at
highest risk from light pollution is important to per-
mit management re sources to be allocated most
effectively (e.g. Fuentes et al. 2011).
We used the 2006 Radiance Calibrated Lights
dataset from the NGDC to address 2 specific aims.
Firstly, we assessed the proportion of nesting marine
turtles within Australia that are exposed to coastal
light pollution as it is detected from space. This pro-
portion was assessed at both a national and ‘popula-
tion management unit’ scale, since it is important
that the severity of threats to specific population
86
Kamrowski et al.: Light pollution and marine turtles
units is determined so as to allow targeted manage-
ment ap proa ches, thereby ensuring that conserva-
tion strategies are as effective as possible (Dobbs et
al. 1999, Wallace et al. 2010). Secondly, we identi-
fied those nesting sites in Australia which may face
the greatest threat from artificial light. This is the
first study of its kind. The results will be beneficial
for both managers and scientists, since this method
allows the identification of nesting locations vulner-
able to coastal light pollution at ecologically rele-
vant scales, which can be used in combination with
existing on-the-ground data to inform and guide
conservation strategies or environmental impact
assessments. The methods utilised in this study will
also prove a useful tool for managers of marine
turtles outside of Australia, in any location where
limited resources require targeted conservation
measures.
MATERIALS AND METHODS
Study species
Marine turtle nesting beaches occur across the
entire northern coast of Australia, from northern
New South Wales to Shark Bay in WA. Six of the 7
extant species of marine turtles (loggerhead Caretta
caretta, green Chelonia mydas, hawksbill Eretmo -
chelys imbricata, olive ridley Le pidochelys olivacea,
flatback Natator depressus, leatherback turtles Der-
mochelys coriacea) nest in Australia, with only the
Kemp’s ridley turtle L. kempii absent. Nesting and
hatchling emergence occur at different times of the
year, depending on the species and population man-
agement unit (Limpus 2009). Due to the minor and
sporadic nesting of leatherback turtles in Australia
this species was not included in our analysis.
Data acquisition
Turtle nesting data
We extracted the locations of nesting beaches for
all turtle species within Australia from the QDERM
(Queensland Department of Environment and Re -
source Management) turtle database, September
2003. These data consisted of geographical informa-
tion system (GIS) point shapefiles, with a geographic
position (latitude/longitude) for each nesting beach,
as well as an estimate of the number of females
breeding each year at the beach. The use of adult
females, excluding adult males and immature turtles,
is a commonly used metric for assessing population
units of marine turtles (Heppell et al. 2003). The esti-
mates used here are the results of numerous studies
(see Limpus 2009 for a review), and are the best
known data available. Gaps in the database were
filled using expert opinion from local government or
industry turtle project staff.
Population unit data
Population genetic structures for green, logger-
head, flatback and hawksbill turtles in Australia have
been extensively investigated (Bowen et al. 1992,
Broderick et al. 1994, Dobbs et al. 1999, Limpus et al.
2000, Dethmers et al. 2006, Conant et al. 2009, Lim-
pus 2009). Only 1 discrete population management
unit of olive ridley turtles is currently recognised in
Australia, although this is likely to evolve as more
genetic research is conducted. There are nu merous
terms in current usage within the scientific literature
to describe population units of marine turtles. We fol-
low the terminology used by Dethmers et al. (2006),
and refer to each population unit as a ‘management
unit’.
Satellite data
We obtained the 2006 DMSP-OLS raster image of
radiance-calibrated night time light data from the
NGDC archive (National Geophysical Data Centre
2006). These data were collected by Satellite F16 and
are the most recent radiance-calibrated night time
light products available. The DMSP satellite flies in a
sun-synchronous low earth orbit (833 km mean alti-
tude), and orbits the planet 14 times each day with a
broad field of view (approximately 3000 km swath
width), allowing complete coverage of the globe to
be obtained in every 24 h period. The OLS sensor
contains a photomultiplier tube (PMT), which inten-
sifies the visible band signal at night, and captures
30 arc second resolution grids. This grid cell size cor-
responds to approximately 1 km2at the equator
(Elvidge et al. 1997, Aubrecht et al. 2010). The night-
time pass occurs between 20:30 and 21:30 h each
night (Elvidge et al. 2001). Turtle nesting and hatch-
ling emergence occur throughout the night, with
peak hatchling emergence occurring between 20:00
and 24:00 h (Limpus 1971, Gyuris 1993). Thus, this
time period is suitable for assessing the risk to turtles
from artificial lights.
87
Endang Species Res 19: 85– 98, 2012
Pre-assessment
Preparation of shapefiles
The night-light data was obtained in a geographic
coordinate system appropriate for global datasets
(GCS_WGS_1984). Once the data pertaining to Aus-
tralia had been extracted using ESRI ArcGIS 9.3.1
(Fig. 1), the data were transformed into the relevant
Australian coordinate system (GCS_GDA_1994),
which matched the geographic coordinate system of
the nesting data. For each management unit, night-
light and turtle nesting data were then further ex -
tracted and projected into the appropriate coordinate
system (GDA_1994_MGA_Zone_49 to 56).
Preparation of night-light pixel data
Pixel values within the radiance-calibrated lights
product were converted into a measure of radiance
(W m−2 sr−1) (sr: steradian) using the conversion fac-
tor provided by the NGDC (see www.ngdc.noaa.gov/
dmsp/ data/ radcal/ readme.txt). The radiance data
were converted into luminance data (cd m−2) to per-
mit a more intuitive measure of night-time light con-
centrations, since radiance (a radiometric unit) de -
scribes all wavelengths of light emitted by a source,
whereas luminance (a photometric unit) is a measure
of the electromagnetic radiation detectable by an ob -
server (Palmer 1999).
Converting between radiance and luminance is
possible, but observers are not equally sensitive to all
wavelengths (Narisada & Schreuder 2004). All pho-
tometry is based on the standard visibility curve (CIE
1932) designed for the photopic (light-adapted) vision
of humans (Narisada & Schreuder 2004), which peaks
at 555 nm. The design of artificial light sources is also
related to this curve, since illumination levels gener-
ated by most light sources result in light-adapted
vision (Zissis et al. 2007).
Recent research has discovered that the visual sen-
sitivity of both adult and hatchling marine turtles
show similarities to human vision. Both are sensitive
to wavelengths in the visible part of the spectrum,
with peak sensitivity found for green wavelengths at
approximately 540 nm in hatchlings (Horch et al.
2008) and at approximately 580 nm in adults (Leven-
son et al. 2004). At present there is no luminosity func-
tion of photopic vision available for turtles; however,
given the similarities in visual sensitivity and also the
wavelengths recorded by the OLS sensor, for the pur-
poses of the present study, it was considered sufficient
to convert between the units using values from the
spectral luminous efficiency for human photopic vision.
Radiance values were converted into luminance
values using the following equation, which repre-
sents a weighting of the radiance spectral term for
each wavelength in relation to the visual response at
that wavelength (Palmer 1999):
(1)
where Xvis the luminous intensity (cd m−2), Kmis the
constant scaling factor (683 for photopic vision;
Hentschel 1994), Xλis the corresponding radiant
intensity (W m−2 sr−1, in nm), Vλis the curve for pho-
topic vision and λis wavelength.
Each pixel could then be classified into a level cor-
responding to a ratio between artificial light and nat-
ural night-time brightness below the atmosphere
(Cinzano et al. 2001a) (Table 1). Natural night-time
brightness varies depending upon numerous factors,
including geographical position, solar activity, time
from sunset and sky area observed (Cinzano et al.
2001b). Since these details were not available for
each nest site, we followed the methodology of Cin-
zano et al. (2001a) and used an average natural
night-time brightness below the atmosphere of 2.52 ×
10−4 cd m−2 (Garstang 1986). The International Astro-
nomical Union (IAU) recommends that night-time
brightness should not be increased by >10 % (ap -
prox imately 200 × 10−6 cd m−2) as a result of artificial
lighting (Smith 1979). Consequently a 10% increase
in night-sky brightness above natural levels is gener-
ally accepted as implying light pollution; this corre-
sponds with Category 2 shown in Table 1.
XK XVd
v=
∞
∫
mλλλ
0
88
Fig. 1. Night-time lights of Australia. Image and data pro-
cessing of night-light data by NOAA’s National Geophysical
Data Center. Defense Meteorological Satellite Program data
collected by the US Air Force Weather Agency
Kamrowski et al.: Light pollution and marine turtles
How bright a light appears to a turtle depends on
several spectral characteristics of the light, i.e. light
intensity, wavelength and turtle spectral sensitivity
(Pendoley 2005). Marine turtle hatchlings are sensi-
tive to very low light intensities across the visible
spectrum (Witherington & Bjorndal 1991), but partic-
ularly be tween violet and green wavelengths (400 to
500 nm). Since the satellite data we used include
wavelengths within this range, we reasonably assume
that light levels categorised as ‘light pollution’ in the
present study are visible to turtles. Moreover, given
that very little light is necessary to disrupt the orien-
tation of hatchlings (Witherington & Martin 2000), we
believe that the threshold of light pollution utilised
here is relevant to turtles.
Analysis of light proximity to nesting locations
Nesting beach sites for each species were overlaid
onto the night-light images, and a buffer was drawn
around each nesting site. The data collected by the
DMSP sensors corresponded to an area greater than
that of actual light sources on the ground (Rodrigues
et al. 2012) due to the phenomenon of ‘skyglow’,
which refers to the dome of light projected upwards
and outwards from urban areas at night (Chalkias et
al. 2006). Skyglow is considered to contribute signif-
icantly to ecological impacts from light pollution
(Rich & Longcore 2006, Kyba et al. 2011). For exam-
ple, light generated by an aluminium refinery in
QLD, Australia, disrupted marine turtle orientation
18 km away (Hodge et al. 2007). Consequently, to
take potential effects of skyglow from urban areas
into account, but allowing for small location inaccu-
racies in overlaying transformed and projected data
layers, we followed the methodology used by Aub -
recht et al. (2008) and used a buffer with a radius of
25 km.
Given the low spatial resolution of the night-time
light data (Elvidge et al. 1997), as well as other fac-
tors which may influence the impact of artificial
lights close to nesting beaches, such as barriers,
cloud cover and moon phase (Salmon & Witherington
1995, Witherington & Martin 2000, Kyba et al. 2011),
2 measures were used to estimate the potential risk
of light pollution faced by each species of nesting
turtle — as a means of avoiding false precision. The
buffer (25 km radius) surrounding each nest site
encompassed approximately 2400 pixels, each of
which possessed a value corresponding to the
amount of light emitted in that area. The mean and
maximum pixel values within each buffer were cal-
culated using the zonal statistics tool and Hawth’s
Tools extension (Beyer 2004) in ArcGIS. These values
were then assigned into one of the light pollution cat-
egories (as per Cinzano et al. 2001a) using the values
given in Table 1. This gave 2 potential risk values for
each site: ‘mean light exposure’ calculated from the
mean pixel value and ‘maximum light exposure’ cal-
culated from the maximum pixel value. Using the
maximum pixel value provides an indication of the
highest amount of light potentially visible to turtles at
each site, and as such is the high-risk scenario. The
mean pixel value was calculated across the entire
area encompassed by each buffer, to effectively
‘smooth out’ the amount of artificial light emitted in
that area (since light levels will be highest in areas
where bright lights are located, decreasing as
distance from the light source increases), hence pro-
viding a diffuse measure of light pollution within a
particular buffer area. This was used to provide a
secondary measure of risk given that nesting turtles
may not be directly exposed to the highest levels of
light present in the immediate area, but would still
likely be susceptible to skyglow effects.
Next, to determine the sites potentially at highest
risk from light pollution for each species and man-
89
Category Pixel value Radiance value Luminance value Ratio over
(risk value) (W m2sr−1) (cd m−2) natural brightness
1 (0) 0−0.6868 0−1.03 × 10−12 0−2.5 × 10−6 0−0.01
2 (0.01) 0.6868 –0.7553 1.03 × 10−12−1.14 × 10−11 2.5 × 10−6 − 2.8 × 10−5 0.01−0.11
3 (0.11) 0.7553−0.9061 1.14 × 10−11−3.43 × 10−11 2.8 × 10−5 − 8.3 × 10−5 0.11−0.33
4 (0.33) 0.9061−1.36 3.43 × 10−11−1.03 × 10−10 8.3 × 10−5 − 2.5 × 10−4 0.33−1
5 (1) 1.36−2.734 1.03 × 10−10−3.11 × 10−10 2.5 × 10−4 − 7.6 × 10−4 1−3
6 (3) 2.734−6.842 3.11 × 10−10−9.34 × 10−10 7.6 × 10−4 − 2.3 × 10−3 3−9
7 (9) 6.842−19.167 9.34 × 10−10 −2 × 10−9 2.3 × 10−3 − 6.8 × 10−3 9−27
8 (27) >19.167 > 2 × 10−9 > 6.8 × 10−3 > 27
Table 1. Quantification of light pollution, using ratios according to Cinzano et al. (2001a). The categories and risk values refer
to the present study
Endang Species Res 19: 85– 98, 2012
agement unit, we calculated the percentage nesting
that occurred at each nesting location, both nation-
ally and within each management unit. Then we
weighted each site for potential risk, by multiplying
the percentage nesting by the mean and maximum
light exposure risk values, to give 2 potential meas-
ures of exposure to light pollution (presented as
median values ± standard deviations).
Data analysis
Data were tested for normality using the Kol-
mogorov-Smirnoff test. Since data were not found to
be normally distributed, comparisons of light expo-
sure between population management units were
assessed using the Mann-Whitney U-test and the
Kruskall-Wallis test. Post hoc pairwise comparisons
of the latter were carried out using Dunn-Bonferroni
tests (Dunn 1964). All data were analysed using IBM
SPSS 20 statistical software.
RESULTS
National light pollution exposure
Nesting sites for loggerhead, green, hawksbill and
flatback turtles in Australia appear to be exposed to
varying degrees of light pollution (Table 2). How-
ever, despite the broad geographic scale of impact,
the majority of marine turtle nesting sites in Australia
appear minimally affected by either level of light
pollution exposure (Table 2).
Management unit light pollution exposure
The above analysis was repeated with the species
nesting site data merged into management units
(Bowen et al. 1992, Broderick et al. 1994, Dobbs et al.
1999, Limpus et al. 2000, Dethmers et al. 2006, Lim-
pus 2009, Wallace et al. 2010).
Loggerheads
There are 2 management units of loggerheads in
Australia: the WA management unit, which occurs
from Dirk Hartog Island to the Muiron Island region,
and the eastern Australian management unit, which
is concentrated on the mainland coast of southeast
QLD, the islands in the southern Great Barrier Reef
(GBR) and minor nesting sites in New Caledonia and
Vanuatu (Limpus 2009).
Using the maximum light exposure values, we
found more than a third of nesting WA log ger heads
and 43.9% of the eastern Australian loggerheads
were potentially exposed to light pollution (Table 3).
Indeed a maximum light pollution weighting of
461.54 occurred for WA loggerheads (307.7 ± 217.6),
which is significantly higher than the maximum
weighted exposure for eastern Australian logger-
heads (max. = 80.6; median = 8.06 ± 31.76; Mann-
Whitney U= <1, n1= 2, n2= 30, p < 0.05).
However, when using the mean light exposure
values, we found that, although the WA loggerheads
appeared relatively unaffected by light pollution,
22% of the nesting sites for the eastern Australian
management unit had a light pollution exposure
90
Ratio over Light Proportion nesting (%)
natural pollution Cc Cm Ei Lo Nd
brightness category Max. Mean Max. Mean Max. Mean Max. Mean Max. Mean
0−0.01 1 61.08 89.5 73.81 85.35 35.58 74.44 90.25 100 32.09 75.93
0.01−0.11 2 0 0 0 0 0 0 0 0 0 0.02
0.11−0.33 3 0 0 0 0.05 0 0 0 0 0 0.16
0.33−1 4 0 0.29 0 2.71 0 0.35 0 0 0 21.07
1−3 5 0 0.29 0 11.79 0 25.22 0 0 0 1.21
3−9 6 9.04 9.33 0.48 0.07 4.87 0 9.3 0 3.39 1.56
9−27 7 9.18 0.58 2.86 0.005 12.88 0 0.45 0 19.1 0.06
>27 8 20.7 0 22.85 0 46.67 0 0 0 45.42 0
Total % exposed to 38.92 10.5 26.19 14.65 64.42 25.56 9.75 0 67.91 24.07
light pollution
Table 2. Proportion of nesting in Australia, by each species, potentially at risk from each category of light pollution, using the
mean (mean light exposure) and maximum (maximum light exposure) pixel values from the radiance calibrated light data
within a 25 km radius buffer surrounding each nest site. Cc: loggerhead Caretta caretta; Cm: green Chelonia mydas; Ei:
hawksbill Eretmochelys imbricata; Lo: olive ridley Lepidochelys olivacea. Nd: flatback Natator depressus
Kamrowski et al.: Light pollution and marine turtles
weighting of 8.96 (2.7 ± 3.84); thus the sites are
potentially at risk from light pollution (Table 3).
Greens
There are 7 recognised green turtles management
units in Australia (Dethmers et al. 2006). Only a small
percentage of nesting sites for 3 of the management
units were determined to be potentially at risk from
light pollution (Table 3). The exception to this was
the North West Shelf management unit in WA, which
showed a large proportion of nesting sites potentially
at risk from both levels of light exposure (39% of the
North West Shelf green turtle nesting areas high-
lighted using the mean light exposure values, and
68%, using the maximum light exposure values).
There was a statistically significant difference be -
tween the maximum light exposure of the 3 green
turtle management units indicated as exposed to
light pollution (Kruskal-Wallis χ2[3, N = 40] = 23.07,
p < 0.01). Pair-wise comparisons indicated that risk
of light pollution for nesting turtles on the North
West Shelf (658.54; 197.6 ± 196.03) was significantly
higher than for all other green turtle management
units. Also, in eastern Australia, the risk of light
pollution for green turtles nesting in the southern
GBR stock (16.93; 1.69 ± 6.96) was significantly
higher than for the northern GBR stock (0.33; 0.22
± 0.15).
Using the mean light exposure values, green tur-
tles nesting in the North West Shelf (24.39; 0 ± 8.1)
are exposed to a significantly higher potential risk
from light pollution compared to green turtles in the
GBR (northern GBR: 0.11; 0.07 ± 0.05; southern GBR:
1.88; 0.19 ± 0.62) (Kruskal-Wallis χ2[2, N = 13] = 7.67,
p < 0.01).
Hawksbills
Three hawksbill turtle management units are
recognised in Australia (Broderick et al. 1994, Dobbs
et al. 1999, Limpus et al. 2000). Using the maximum
light exposure values, a large proportion of all 3 were
potentially exposed to light pollution (Table 3). Most
notable was hawksbill nesting in WA, for which
99.8% of nesting appeared to be exposed. The maxi-
mum light pollution weighting for hawksbills in WA
(1225.42; 673.98 ± 636.75) was significantly higher
than for hawksbills in the Gulf of Carpentaria (53.05;
17.68 ± 18.12), and for hawksbills in the Torres Strait
and northern GBR (84.59; 0.85 ± 21.99) (Kruskal-
Wallis χ2[2, N = 46] = 23.88, p < 0.01).
When employing the mean light exposure values, a
large proportion of hawksbill nesting in WA remained
highlighted as being at potential risk from light pollu-
tion, with an exposure weighting of 45.39 (4.54 ±
23.58), but the other management units were not de-
termined to be at significant potential risk. The small
91
Turtle species Population Risk from mean Risk from maximum
management unit light exposure light exposure
(%, using mean pixel value) (%, using max. pixel value)
Loggerhead Western Australia 0 34.2
Eastern Australia 21.5 43.9
Green North West Shelf 39 68.3
Scott Reef 0 0
Ashmore Reef 0 0
Gulf of Carpentaria 0 4.5
Northern GBR <1 <1
Coral Sea 0 0
Southern GBR 2.2 3.8
Hawksbill Western Australia 54.5 99.8
Gulf of Carpentaria 3.5 41.5
Northern GBR & Torres Strait 0 31.4
Olive ridley Northern Australia 0 9.8
Flatback North West Shelf 59.06 87.4
Western Northern Territory 0 0
Gulf of Carpentaria & Torres Strait <1 61
Eastern Australia 24.2 50.1
Table 3. Proportion of each population management unit of marine turtles in Australia located in nesting areas potentially ex-
posed to artificial lights brighter than the threshold level of light pollution, i.e. light exposure of Category 2 or above (see
Table 1). GBR: Great Barrier Reef
Endang Species Res 19: 85– 98, 2012
sample size of affected sites precluded statistical
analysis, but the medians indicated that the WA man-
agement unit remains at higher risk from light pollu-
tion than hawksbills nesting in northern Australia.
Olive ridleys
There is currently only 1 recognised management
unit of olive ridley turtles in Australia (Limpus 2009).
The nesting sites for this management unit appeared
relatively unaffected by light pollution. The mean
light exposure values indicated that none of the nest-
ing sites appeared to be exposed to light pollution,
and, using the maximum light exposure values, only
4 out of 25 nesting sites (9.8% of nesting olive ridleys)
were potentially exposed to light pollution of Cate-
gories 6 and 7 (Table 2).
Flatbacks
Four flatback turtle management units are cur-
rently recognised in Australia (Limpus 2009), al -
though with on-going genetic research this is likely
to evolve over time. Flatback turtles which nest in the
western Northern Territory appeared largely unex-
posed to light pollution (Table 3). However, for the
other 3 management units when using the maximum
light exposure values, large nesting proportions
appeared potentially at risk from light pollution,
whereas only the North West Shelf and eastern Aus-
tralia management units were identified to be at
potential risk when employing the mean light expo-
sure values.
The maximum light exposure values gave a maxi-
mum weighting of 637.8 for flatback turtles on the
North West Shelf (330 ± 294.3). This was significantly
higher than exposure weightings obtained for flat-
back turtles nesting in either the Gulf of Carpentaria
and Torres Strait (97.69; 1.51 ± 13.58) or eastern Aus-
tralia (94.57; 4.73 ± 23). Eastern Australian sites
appeared significantly more light-exposed than Gulf
of Carpentaria and Torres Strait sites (Kruskal-Wallis
χ2[2, N = 115] = 49.58, p < 0.01).
When using the mean light exposure values, flat-
back nesting sites on the North West Shelf appeared
to be exposed to significantly more light pollution
(23.62; 4.78 ± 9.46) than sites in eastern Australia
(5.25; 0.53 ± 1.98) (Mann-Whitney U= 16, n1= 4, n2=
39, p < 0.01).
Region
For each species with multiple management units
within Australia, it was the management units nest-
ing in WA that were exposed to the highest levels of
light pollution (Table 4). In particular the Dampier
Archipelago, Barrow Island, Montebello Islands and
Cape Range Ningaloo were identified as potential
high-risk nesting sites for >1 species (Figs. 2 & 3).
DISCUSSION
Marine turtles spend 100% of their critical breed-
ing life-history phase (egg laying, incubation and
hatchling emergence) out of the water on beaches.
Moreover, turtles migrate from dispersed foraging
grounds to aggregate at these breeding sites (e.g.
Limpus et al. 1992). Thus, effective, long-term con-
servation strategies require the protection of these
developmental habitats (Troëng & Rankin 2005).
Since successful turtle nesting is strongly hindered
by the presence of artificial light (Witherington &
Martin 2000) and the effective management of light
pollution adjacent to turtle nesting sites may be both
expensive and time-intensive (e.g. Fuentes et al.
2009), the identification of nesting sites at greatest
risk from light pollution is crucial to ensure that lim-
ited conservation resources are allocated most effec-
tively (e.g. Fuentes et al. 2011).
We used satellite imaging as a broad-scale tool for
the identification and comparison of nesting locations
potentially vulnerable to coastal light pollution at
ecologically relevant scales. An important caveat to
our study, given the coarse spatial scale of the dataset
utilized, is that beachfront lighting in an otherwise
undeveloped area may not register in the satellite
data, but would retain the potential to disrupt turtle
92
Population Mean light Max. light
management exposure exposure
units
1 North West Shelf Western Australian
flatback turtles hawksbill turtles
2 Western Australian North West Shelf
hawksbill turtles flatback turtles
3 North West Shelf North West Shelf
green turtles green turtles
Table 4. The 3 marine turtle management units in Australia
potentially most exposed to light pollution, using the mean
(mean light exposure) and maximum (maximum light
exposure) pixel values
Kamrowski et al.: Light pollution and marine turtles
nesting (Witherington & Martin 2000). However,
lights from very small residential settlements (pop -
ulations of <300 people) in remote regions of
Australia — including islands of the Torres Strait
where no industry or commercial entities exist —
were picked up by the satellite data. Therefore, it is
unlikely that significant sources of potentially disori-
enting light exist in Australia which were not identi-
fied in the present study.
Furthermore, an examination of our
data in light of evidence regarding the
beach-scale impact of light pollution in
Australia supports the value of our
methodology. We determined that
nesting sites on the North West Shelf
of WA and along the Woongarra coast
of QLD were the sites facing the high-
est potential risk from light pollution
Australia-wide, with nest sites in
northern Australia appearing to be
minimally exposed to light pollution.
In his comprehensive review of marine
turtles within Australia, Limpus (2009)
evaluated the threat of light pollution
for each species of turtle, using data
and observations from researchers
working on the ground. Reflecting our
data, Limpus (2009) found no evidence
of turtles disrupted by artificial light in
northern Australia, but highlighted
the Woongarra coast of QLD and the
North West Shelf in WA as areas
where disorientation of hatchlings
regularly occurred due to the presence
of artificial lights. Consequently, the
method we have presented offers a
useful means of highlighting particu-
lar regions, over a large spatial scale,
where marine turtle nesting may be at
risk from light pollution. Our method
also allows for the magnitude of poten-
tial light pollution risk to be compared
across nest sites. Once potentially
high-risk sites for management units
have been identified, the next step for
managers should be an on-the-ground
assessment to confirm the risk identi-
fied by the broad-scale analysis pre-
sented here, and to subsequently
determine necessary beach-specific
management actions.
Overall our findings indicate that
there is large spatial variation in levels
of coastal light pollution across Australia, which
might be expected to cause disruption to marine tur-
tles. Although the majority of marine turtle nesting in
Australia appears to be minimally affected by light
pollution, large proportions of nesting hawksbill, flat-
back, green and loggerhead turtles do appear to be
exposed to light pollution, especially in WA and
along the urban coast of Queens land. Moreover, tur-
tles at these sites are potentially exposed to light sub-
93
Fig. 2. The 10 nesting sites (by species) in Australia potentially at highest risk
from maximum light exposure (maximum pixel values), with light pollution
exposure values (percent nesting × risk value) in parentheses. Cc: loggerhead
Caretta caretta; Cm: green Chelonia mydas; Ei: hawksbill Eretmochelys
imbricata; Nd: flatback Natator depressus
Fig. 3. The 10 nesting sites (by species) in Australia potentially at highest risk
from mean light exposure (mean pixel values), with light pollution exposure
values (percent nesting × risk value) in parentheses. Six of the sites occurred
in SE QLD, 4 in WA (values inset left, see Fig. 2 for locations). Cc: loggerhead
Caretta caretta; Cm: green Chelonia mydas; Ei: hawksbill Eretmochelys
imbricata; Nd: flatback Natator depressus
Endang Species Res 19: 85– 98, 2012
stantially brighter than natural night-time brightness,
with most affected nesting sites potentially exposed
to light pollution of Category 5 or higher (>1 to 3
times brighter than natural night-time brightness).
This is important be cause ecological and behavioural
studies have found that hatchling disorientation can
be caused by very low levels of artificial light (With-
erington & Martin 2000). The pervasive levels of light
pollution we found would be expected to disrupt tur-
tle orientation at these sites.
Certain management units appear to face extreme
potential risk, with 99.8% of hawksbill turtle nesting
sites and 87.4% of flatback turtle nesting sites in WA
determined to be at risk from light pollution. This is
substantially higher than previous estimates of 12
and 42% for hawksbill and flatback turtles, respec-
tively, in the region of the Barrow, Lowendal and
Montebello Islands of WA (Pendoley 2005). However,
where we calculated exposure within an area 25 km
in radius from the nesting site, Pendoley (2005) con-
sidered the effect of lights within a radius of 1.5 km —
a conservative radius considering the distance over
which lights have been known to disrupt turtle be -
haviour on land (Hodge et al. 2007) and may poten-
tially affect hatchling behaviour in the sea. Turtle
hatchlings swim slowly, covering only 1.5 km h−1 or
less (Frick 1976, Salmon & Wyneken 1987). However,
swimming hatchlings show oriented swimming be -
haviour for longer than 24 h (Salmon & Wyneken
1987) and, in the absence of wave cues to guide them
offshore, have been found to be more susceptible to
disorientation from onshore light cues (Lorne &
Salmon 2007). Consequently, in the absence of wave
cues, artificial lights may influence the orientation of
swimming hatchlings over distances >1.5 km. The
high proportion of hawksbill and flatback turtle nest-
ing sites in WA identified as being at potential risk in
the present study highlights the need for manage-
ment and policy approaches that consider synergistic
and cumulative impact.
We found that within Australia a few nesting sites
in WA, which support nesting by multiple species,
appear to be the sites most vulnerable to light pollu-
tion—namely the Dampier Archipelago, the Monte-
bello Islands, Varanus Island and Barrow Island. The
presence of light pollution at these sites is well
known. This is one of WA’s, and indeed Australia’s,
most productive regions for resource extraction, pro-
cessing and shipping, with 59% of WA’s oil and 93%
of WA’s gas being produced on the North West Shelf
(Department of Environment and Conservation 2007).
The influence of lights and flares from hydrocarbon
industrial plants has been categorised as a current
major pressure on turtles in this region (Pendoley
2000, Environment Australia 2003, Department of
Environment and Conservation 2007, Environmental
Protection Agency 2010), and State Government leg-
islation, plus industry-specific management plans,
are in place to regulate the of use of appropriate
lighting by existing and future industry (Department
of Environment and Conservation 2007, 2008, Chev -
ron Australia 2009, Environmental Protection Agency
2010, BHP Billiton 2011).
Despite acknowledgement of the existence of light
pollution in this region, we demonstrate that nest site
exposure to light pollution may be far higher in WA
than elsewhere in Australia, and, collectively, it could
impact turtles at ecological scales, since multiple nest-
ing sites appear affected within turtle management
units. This indicates that rigorous light pollution man-
agement is vital, particularly given the im portance of
the turtle management units which nest here. The WA
management units of hawksbill, green, loggerhead
and flatback turtles are globally significant for their
respective species (Seminoff 2002, Mortimer & Don-
nelly 2008). Moreover, our results are conservative
due to our use of light data from 2006; since that time
development of the region has continued.
In 1 recent liquefied natural gas (LNG) develop-
ment, the proponents were legally obliged under
ministerial conditions attached to their Australian
Government approval to develop management plans
for marine turtles and develop and implement miti-
gation plans for light pollution. Under these plans,
site-specific light pollution is audited annually
(Chev ron Australia 2009). Yet, although light pollu-
tion in this region seems to be being addressed by
individual producers at site-specific scales (e.g. Sin-
clair Knight Merz 2008, Chevron Australia 2009, BHP
Billiton 2011), the cumulative effect of extreme light
levels over a small geographic region, or as it relates
to specific turtle management units, is not addressed
by State or Australian Government legislation or pol-
icy (Department of Environment and Conservation
2008).
We also found that nesting sites in eastern Aus-
tralia appeared to be at high risk from light pollution,
particularly in the case of loggerhead turtles along
the Woongarra coast of south-east QLD. Interestingly
these nesting locations were only identified as being
at high potential risk when using the mean light
exposure values, i.e. the mean pixel value within the
25 km buffer. This suggests that light pollution in
eastern Australia may be characterised by areas of
widespread, moderate levels of light pollution from
dispersed urban settlements, as opposed to small
94
Kamrowski et al.: Light pollution and marine turtles
areas of high levels of localised light pollution from
intense industrial development on an otherwise rela-
tively unsettled coastline in WA. This has implica-
tions for management in that it may be more econom-
ically and logistically feasible to implement light
mitigation in WA, by targeting small areas of high
light pollution produced by a limited number of con-
tributors, rather than targeting a larger area produc-
ing moderate levels of light pollution, with multiple
contributors (e.g. Fuentes et al. 2011).
Industrial development is increasing along the
eastern Australian coast, as well as in other turtle
nesting locations worldwide, including Qatar (Tayab
& Quiton 2003) and India (Fernandes 2008). Given
the findings from the present study, which suggest
that the amount of light pollution produced by similar
existing industrial developments in WA may pose a
very high risk to nesting marine turtles, the adequate
management of light generated by proposed and on-
going industrial developments should be considered
extremely important by managers and policy mak-
ers. One of the challenges currently faced by indus-
try, regulators and researchers involved with turtle
conservation is the lack of monitoring tools to exam-
ine low, ecologically relevant, light levels, or tools to
test the effects of skyglow.
By virtue of the collection method, only night-time
light levels on cloud-free nights are represented by
the satellite data (Elvidge et al. 2001). A recent study
has demonstrated that cloud cover substantially
increases skyglow, since unused light escaping up -
wards into the atmosphere is reflected back down to
Earth by clouds (Kyba et al. 2011). The authors argue
that investigations into the ecological effects of light
pollution need to take cloud coverage into considera-
tion. Thus, light pollution levels and the subsequent
impacts of this light at turtle nesting sites on cloudy
nights may be even higher than suggested by our
findings.
CONCLUSIONS
Light pollution is an indisputable problem for
marine turtles, and, given existing and continuing
coastal development along many of the world’s turtle
nesting beaches, it is also likely to be a pervasive
issue. Studies investigating the impacts of light pollu-
tion on marine turtles are numerous; yet, since most
of this research is beach or region specific, under-
standing the risks posed to breeding marine turtles at
a management unit scale, from light generated by
different producers, has not been possible.
Our study is the first of its kind. The methodology
we present provides a useful first step for effectively
managing the disruptive influence of light pollution
on marine turtles, at an ecologically relevant scale.
The large spatial scale we used emphasises the sig-
nificant risk that concentrated light produced by
industrial developments, and diffuse light generated
by urban complexes, may pose to nesting marine tur-
tles. We also highlight the regions of Australia where
turtle nesting appears to be at highest risk from light
pollution, namely southerly nesting sites on both the
west and east coasts, with sites in northern Australia
least affected.
In view of the multitude of threats faced by turtles,
consideration of this information is extremely rele-
vant for managers, especially in regions of planned
industrial development. This is particularly impor-
tant in regions with multiple contributors to artificial
light production, since cumulative light levels may
not be addressed in management plans. Further-
more, the identification of concentrated and diffuse
light pollution indicates that management strategies
may need to be tailored depending on the sources
generating the artificial light.
We recommend that light-mitigation strategies be
implemented as standard as development increases
along the Australian coastline, and we urge man-
agers of marine turtles elsewhere to recognise the
huge potential for disruption that light generated by
industrial and urban developments may cause.
Acknowledgements. We thank SEQ Catchments and
QDERM for providing the nesting data, P. Whittock for infor-
mation on nest sites in WA, P. Ridd for advice regarding the
conversion of light from radiance to luminance values and
D. Ziskin at NOAA for assistance with the night-light data
files. We also thank J. Hazel for useful comments, and A.
Edwards for help with the figures. Advice from 2 anonymous
reviewers greatly improved the manuscript. We acknowl-
edge the image and data processing of night-light data by
NOAA’s National Geophysical Data Center and the DMSP
data collected by the US Air Force Weather Agency. This
manuscript forms part of R.L.K.’s PhD research at James
Cook University. R.L.K. is supported by the Northcote Trust
Graduate Scholarship Scheme.
LITERATURE CITED
Aubrecht C, Elvidge CD, Eakin CM (2008) Earth observa-
tion based assessment of anthropogenic stress to coral
reefs — a global analysis. Proc 2008 IGARRS 4:367–370
Aubrecht C, Jaiteh M, de Sherbinin A (2010) Global assess-
ment of light pollution impact on protected areas.
CIESIN/AIT Working Paper, Columbia University, New
York, NY. Available at www.ciesin.columbia.edu/
publications .html
95
Endang Species Res 19: 85– 98, 2012
Australian Bureau of Statistics (2012) Regional population
growth, Australia, 2009−2012. Report 3218.0, Australian
Bureau of Statistics, Canberra
Badarinath KVS, Sharma AR, Kharol SK (2011) Forest fire
monitoring and burnt area mapping using satellite data:
a study over the forest region of Kerala State, India. Int J
Remote Sens 32: 85−102
Bertolotti L, Salmon M (2005) Do embedded roadway lights
protect sea turtles? Environ Manag 36: 702−710
Beyer HL (2004) Hawth’s analysis tools for ArcGIS. Avail-
able at www.spatialecology.com/htools (accessed 22 Feb
2011)
BHP Billiton (2011) Marine turtle management plan. Avail-
able at www.bhpbilliton.com/home/aboutus/ regulatory /
Documents/perAppendixA1MarineTurtleManagement
Plan.pdf (accessed 2 Nov 2011)
Bird BL, Branch LC, Miller DL (2004) Effects of coastal light-
ing on foraging behavior of beach mice. Conserv Biol 18:
1435−1439
Booth DT, Evans A (2011) Warm water and cool nests are
best. How global warming might influence hatchling
green turtle swimming performance. PLoS ONE 6: e23162
Bowen BW, Meylan AB, Ross JP, Limpus CJ, Balazs GH,
Avise JC (1992) Global population structure and natural
history of the green turtle (Chelonia mydas) in terms of
matriarchal phylogeny. Evolution 46: 865−881
Broderick D, Moritz C, Miller J, Guinea M, Prince R, Limpus
C (1994) Genetic studies of the hawksbill turtle Eretmo -
chelys imbricata: evidence for multiple stocks in Aus-
tralian waters. Pac Conserv Biol 1: 123−131
Bustard HR, Tognetti KP (1969) Green sea turtles: a discrete
simulation of density-dependent population regulation.
Science 163: 939−941
Chalkias C, Petrakis M, Psiloglou B, Lianou M (2006) Mod-
elling of light pollution in suburban areas using remotely
sensed imagery and GIS. J Environ Manag 79: 57−63
Chatto R, Baker B (2008) The distribution and status of mar-
ine turtle nesting in the Northern Territory. Parks and
Wildlife Service of the NT, Palmerston
Chevron Australia (2009) Gorgon gas development and
Jansz feed gas pipeline long-term marine turtle manage-
ment plan G1-NT-PLNX0000296, Chevron Australia Pty
Ltd. Available at www. chevronaustralia. com/ Libraries/
Chevron _Documents/ Gorgon _ Long-term _Marine_ Turtle
_ Management _ Plan .pdf. sflb. ashx (accessed 4 November
2011)
CIE (International Commission on Illumination) (1932)
Receuil des travaux et compte rendue des scéances,
huitième session Cambridge — Septembre 1931. Cam-
bridge University Press, Cambridge
Cinzano P, Falchi F, Elvidge CD (2001a) The first World
Atlas of the artificial night sky brightness. Mon Not R
Astron Soc 328: 689−707
Cinzano P, Falchi F, Elvidge CD (2001b) Naked eye star vis-
ibility and limiting magnitude mapped from DMSP-OLS
satellite data. Mon Not R Astron Soc 323: 34−46
Conant TA, Dutton PH, Eguchi T, Epperly SP and others
(2009) Loggerhead sea turtle (Caretta caretta) 2009 status
review under the US Endangered Species Act. Report of
the loggerhead biological review team. National Marine
Fisheries Service, Silver Spring, MD
Department of Environment and Conservation (2007) Man-
agement plan for the Montebello/Barrow Islands marine
conservation reserves, 2007−2017. Department of Envi-
ronment and Conservation, Perth
Department of Environment and Conservation (2008) Draft
marine turtle recovery plan for Western Australia. In:
Western Australian wildlife management program.
Department of Environment and Conservation, Perth
Dethmers KEM, Broderick D, Moritz C, Fitzsimmons NN
and others (2006) The genetic structure of Australasian
green turtles (Chelonia mydas): exploring the geograph-
ical scale of genetic exchange. Mol Ecol 15: 3931−3946
Dobbs K, Miller J, Limpus C, Landry A Jr (1999) Hawksbill
turtle, Eretmochelys imbricata, nesting at Milman Island,
northern Great Barrier Reef, Australia. Chelonian Con-
serv Biol 3: 344−361
Dunn OJ (1964) Multiple comparisons using rank sums.
Technometrics 6: 241−252
Elvidge CD, Baugh KE, Hobson VR, Kihn EA, Kroehl HW,
Davis ER, Cocero D (1997) Satellite inventory of human
settlements using nocturnal radiation emissions: a contri-
bution for the global toolchest. Glob Change Biol 3:
387−395
Elvidge CD, Imhoff ML, Baugh KE, Hobson VR and others
(2001) Night-time lights of the world: 1994−1995. ISPRS J
Photogramm Remote Sens 56: 81−99
Elvidge CD, Cinzano P, Pettit DR, Arvesen J and others
(2007) The Nightsat mission concept. Int J Remote Sens
28: 2645−2670
Environment Australia (2003) Recovery plan for marine tur-
tles in Australia. Environment Australia Marine Species
Section, Canberra
Environmental Protection Agency (2010) Environmental
assessment guideline for protecting marine turtles from
light impacts, No. 5. Environmental Protection Agency,
Perth
Fernandes A (2008) IUCN-TATA partnership — undermin-
ing conservation. Mar Turtle Newsl 121: 20−21
Frick J (1976) Orientation and behaviour of hatchling green
turtles (Chelonia mydas) in the sea. Anim Behav 24:
849−857
Fuentes MMPB, Maynard JA, Guinea M, Bell IP, Werdell PJ,
Hamann M (2009) Proxy indicators of sand temperature
help project impacts of global warming on sea turtles in
northern Australia. Endang Species Res 9: 33−40
Fuentes M, Limpus C, Hamann M (2011) Vulnerability of
sea turtle nesting grounds to climate change. Glob
Change Biol 17: 140−153
Garstang RH (1986) Model for artificial night-sky illumina-
tion. Publ Astron Soc Pac 98: 364−375
Gyuris E (1993) Factors that control the emergence of green
turtle hatchlings from the nest. Wildl Res 20: 345−353
Hamann M, Jessop TS, Schäuble CS (2007) Fuel use and
corticosterone dynamics in hatchling green sea turtles
(Chelonia mydas) during natal dispersal. J Exp Mar Biol
Ecol 353: 13−21
Hamann M, Grech A, Wolanski E, Lambrechts J (2011)
Modelling the fate of marine turtle hatchlings. Ecol
Model 222: 1515−1521
Harewood A, Horrocks J (2008) Impacts of coastal develop-
ment on hawksbill hatchling survival and swimming suc-
cess during the initial offshore migration. Biol Conserv
141: 394−401
Hennessy K, Fitzharris B, Bates BC, Harvey N and others
(2007) Australia and New Zealand. In: Parry ML,
Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE
(eds) Climate change 2007: impacts, adaptation and vul-
nerability. Contribution of Working Group II to the 4th
assessment report of the Intergovernmental Panel on Cli-
96
➤
➤➤
➤
➤➤
➤
➤➤
➤➤
➤
➤
➤➤
➤
➤
➤
➤
➤
➤
➤
➤
➤
➤
➤
➤
Kamrowski et al.: Light pollution and marine turtles
mate Change. Cambridge University Press, Cambridge,
p 507−540
Hentschel HJ (1994) Licht und Beleuchtung: Theorie und
Praxis der Lichttechnik, 4. Auflage. Hüthig, Heidelberg
Heppell SS, Snover ML, Crowder LB (2003) Sea turtle popu-
lation ecology. In: Lutz PL, Musick JA, Wyneken J (eds)
The biology of sea turtles. CRC Press, Boca Raton, FL,
p 275−306
Hodge W, Limpus CJ, Smissen P (2007) Queensland turtle
conservation project: Hummock Hill Island nesting turtle
study December 2006. In: Conservation technical and
data report. Environmental Protection Agency, Brisbane,
p 1−10
Horch KW, Gocke JP, Salmon M, Forward RB (2008) Visual
spectral sensitivity of hatchling loggerhead (Caretta
caretta L.) and leatherback (Dermochelys coriacea L.)
sea turtles, as determined by single-flash electroretinog-
raphy. Mar Freshw Behav Physiol 41: 107−119
Kramer KM, Birney EC (2001) Effect of light intensity on
activity patterns of Patagonian leaf-eared mice, Phyllotis
xanthopygus. J Mammal 82: 535−544
Kyba CCM, Ruhtz T, Fischer J, Holker F (2011) Cloud cover-
age acts as an amplifier for ecological light pollution in
urban ecosystems. PloS ONE 6:e17307
Levenson DH, Eckert SA, Crognale MA, Deegan JF II,
Jacobs GH (2004) Photopic spectral sensitivity of green
and loggerhead sea turtles. Copeia 2004: 908−914
Limpus C (1971) The flatback turtle, Chelonia depressa Gar-
man in southeast Queensland, Australia. Herpetologica
27: 431−446
Limpus C (2009) A biological review of Australian marine
turtles. Environmental Protection Agency, Brisbane
Limpus C, Miller J, Paramenter C, Reimer D, McLachlan N,
Webb R (1992) Migration of green (Chelonia mydas) and
loggerhead (Caretta caretta) turtles to and from eastern
Australian rookeries. Wildl Res 19: 347−357
Limpus C, Miller J, Chatto R (2000) Distribution and abun-
dance of marine turtle nesting in northern and eastern
Australia. In: Final report for Australian hawksbill turtle
population dynamics project. Queensland Parks and
Wildlife Service, Brisbane, p 19−37
Lorne JK, Salmon M (2007) Effects of exposure to artificial
lighting on orientation of hatchling sea turtles on the
beach and in the ocean. Endang Species Res 3: 23−30
Luck GW (2007) The relationships between net primary pro-
ductivity, human population density and species conser-
vation. J Biogeogr 34: 201−212
Magyar T (2008) The impact of artificial lights and anthro-
pogenic noise on loggerheads (Caretta caretta) and
green turtles (Chelonia mydas), assessed at index nesting
beaches in Turkey and Mexico. PhD thesis, University of
Bonn
Mortimer J, Donnelly M (2008) Marine Turtle Specialist
Group 2007 IUCN Red List status assessment, hawksbill
turtle (Eretmochelys imbricata). IUCN, Marine Turtle
Specialist Group, Gland
Nagatani I (2010) A methodology to create DMSP-OLS
night-time mosaic image for monitoring fishing boats.
Proc 30th Asia-Pacific advanced network meeting, Dec
2010, p 143−152. Available at: http:// apan. upm. edu. my/
DMSP/ APAN_31_Nagatani.pdf (accessed 16 Apr 2011)
Narisada K, Schreuder D (2004) Light pollution handbook.
Springer, Dordrecht
National Geophysical Data Centre (2006) Global radiance
calibrated nighttime lights. NOAA, Washington, DC
Nicholls RJ (1995) Coastal megacities and climate change.
GeoJournal 37: 369−379
Palmer JM (1999) Radiometry and photometry FAQ.
Available at: http://employeepages.scad.edu/ ~kwitte/
documents / Photometry_FAQ.PDF (accessed 23 Jun 2011)
Pendoley K (2000) The influence of gas flares on the orienta-
tion of green turtle hatchlings at Thevenard Island, West-
ern Australia. In: Pilcher N, Ghazally I (eds) Proc 2nd
ASEAN symposium and workshop on sea turtle biology
and conservation. ASEAN Academic Press, Kotal Kina-
balu, p 130−142
Pendoley K (2005) Sea turtles and the environmental man-
agement of industrial activities in north west Western
Australia. Murdoch University, Perth
Peters A, Verhoeven KJF (1994) Impact of artificial lighting
on the seaward orientation of hatchling loggerhead tur-
tles. J Herpetol 28: 112−114
Pilcher NJ, Enderby S, Stringell T, Bateman L (2000)
Nearshore turtle hatchling distribution and predation in
Sabah, Malaysia. In: Kalb H, Wibbels T (eds) Proc 19th
annual sea turtle symposium. NOAA Tech Mem NMFS-
SEFSC 443: 27−29
Putman NF, Bane JM, Lohmann KJ (2010) Sea turtle nesting
distributions and oceanographic constraints on hatchling
migration. Proc Biol Sci 277: 3631−3637
Rich C, Longcore T (2006) Ecological consequences of artifi-
cial night lighting. Island Press, Washington, DC
Rodrigues P, Aubrecht C, Gil A, Longcore T, Elvidge C
(2012) Remote sensing to map influence of light pollution
on Cory’s shearwater in São Miguel Island, Azores
Archipelago. Eur J Wildl Res 58: 147−155
Salmon M (2003) Artificial night lighting and sea turtles.
Biologist 50: 163−168
Salmon M (2006) Protecting sea turtles from artificial night
lighting at Florida’s oceanic beaches. In: Rich C, Long-
core T (eds) Ecological consequences of artificial night
lighting. Island Press, Washington, DC
Salmon M, Witherington B (1995) Artificial lighting and
seafinding by loggerhead hatchlings: evidence for lunar
modulation. Copeia 1995: 931−938
Salmon M, Wyneken J (1987) Orientation and swimming
behaviour of hatchling loggerhead sea turtles (Caretta
caretta L.) during their offshore migration. J Exp Mar
Biol Ecol 109: 137−153
Salmon M, Witherington BE, Elvidge CD (2000) Artificial
lighting and the recovery of sea turtles. In: Pilcher N,
Ismail G (eds) Sea turtles of the Indo-Pacific: research,
management and conservation. ASEAN Academic Press,
London, p 25−34
Seminoff J (2002) IUCN Red List global status assessment,
green turtle Chelonia mydas. IUCN Marine Turtle Spe-
cialist Group Review, IUCN, Gland
SEQ Catchments (2010) Summary report on: managing
what matters, the cost of environmental decline in south
east Queensland. SEQ Catchments Ltd, Brisbane
Smith FG (1979) Report and recommendations of IAU Com-
mission 50, Reports on Astronomy. Int Astronom Union
Trans A, XVIIA: 218–222
Sinclair Knight Merz (2008) Pluto LNG development sea tur-
tle management plan. Project No. WV03424.010. Wood-
side Energy Ltd, Perth
Stapput K, Wiltschko W (2005) The sea-finding behavior of
hatchling olive ridley sea turtles, Lepidochelys olivacea,
at the beach of San Miguel (Costa Rica). Naturwis-
senschaften 92: 250−253
97
➤
➤
➤
➤
➤
➤
➤
➤
➤➤
➤
➤
➤➤
Endang Species Res 19: 85– 98, 2012
Steck TL, Bartelmus P, Sharma A (2010) Human population
explosion. In: Cleveland CJ (ed) Encyclopedia of Earth.
Environmental Information Coalition, National Council
for Science and the Environment, Washington, DC.
Available at: www.eoearth.org/ article/ Human_ population
_explosion?topic=54245 (accessed 27 Mar 2011)
Tayab MR, Quiton P (2003) Marine turtle conservation ini-
tiatives at Ras Laffan Industrial City, Qatar (Arabian
Gulf). Mar Turtle Newsl 99: 14−15
Troëng S, Rankin E (2005) Long-term conservation efforts
contribute to positive green turtle Chelonia mydas nest-
ing trend at Tortuguero, Costa Rica. Biol Conserv 121:
111−116
Tuxbury SM, Salmon M (2005) Competitive interactions
between artificial lighting and natural cues during
seafinding by hatchling marine turtles. Biol Conserv 121:
311−316
Wallace BP, DiMatteo AD, Hurley BJ, Finkbeiner EM and
others (2010) Regional management units for marine tur-
tles: a novel framework for prioritizing conservation and
research across multiple scales. PLoS ONE 5: e15465
Witherington B (1991) Orientation of hatchling loggerhead
turtles at sea off artificially lighted and dark beaches. J
Exp Mar Biol Ecol 149: 1−11
Witherington B, Bjorndal KA (1991) Influences of wave-
length and intensity on hatchling sea turtle phototaxis:
implications for sea-finding behavior. Copeia 1991:
1060−1069
Witherington B, Martin RE (2000) Understanding, assessing,
and resolving light-pollution problems on sea turtle
nesting beaches, 2nd edn., rev. Tech Rep TR-2. Florida
Fish and Wildlife Conservation Commission, Marine
Research Institute, St. Petersburg, FL, p 1−73
Wyneken J, Salmon M, Fisher L, Weege S (2000) Managing
relocated sea turtle nests in open beach hatcheries. Les-
sons in hatchery design and implementation in Hillsboro
Beach, Broward County, Florida. In: Kalb H, Wibbels T
(eds) Proc 19th annual sea turtle symposium. NOAA
Tech Mem NMFS-SEFSC 443: 193−194
Ziskin D, Aubrecht C, Elvidge CD, Tuttle B, Baugh KE,
Ghosh T (2008) Encroachment of human activity on sea
turtle nesting sites. In: Proceedings of the rall meeting
2008. American Geophysical Union, Washington, DC,
p 361
Zissis G, Ruscassie R, Aubes M (2007) The quest of the per-
fect light source. Ing Iluminatului 9: 71−78
98
Editorial responsibility: Brendan Godley,
University of Exeter, Cornwall Campus, UK
Submitted: June 14, 2012; Accepted: September 4, 2012
Proofs received from author(s): November 21, 2012
➤
➤
➤
➤
➤
