Using non-systematic surveys to investigate effects of regional climate
variability on Australasian gannets in the Hauraki Gulf, New Zealand
Mridula Srinivasan, Mariela Dassis, Emily Benn, Karen A. Stockin,
Emmanuelle Martinez, Gabriel E. Machovsky-Capuska
DOI: doi: 10.1016/j.seares.2015.02.004
Reference: SEARES 1340
To appear in: Journal of Sea Research
Received date: 5 September 2014
Revised date: 16 January 2015
Accepted date: 9 February 2015
Please cite this article as: Srinivasan, Mridula, Dassis, Mariela, Benn, Emily,
Stockin, Karen A., Martinez, Emmanuelle, Machovsky-Capuska, Gabriel E., Using
non-systematic surveys to investigate eﬀects of regional climate variability on Aus-
tralasian gannets in the Hauraki Gulf, New Zealand, Journal of Sea Research (2015),
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Using non-systematic surveys to investigate effects of regional climate
variability on Australasian gannets in the Hauraki Gulf, New Zealand
Mridula Srinivasan1,*, Mariela Dassis2, Emily Benn3, Karen A. Stockin4, Emmanuelle Martinez5,3, Gabriel E.
1 National Marine Fisheries Service, 1315 East-West Highway, Silver Spring, MD 20910, USA
2 Facultad de Ciencias Exactas y Naturales, Instituto de Investigaciones Marinas y Costeras, Universidad Nacional de
Mar del Plata-CONICET, Funes 3350 (7600), Mar del Plata, Argentina
3 School of Biological Sciences and The Charles Perkins Centre, University of Sydney, Sydney, Australia
4 Coastal-Marine Research Group, Institute of Natural and Mathematical Sciences, Massey University, Auckland,
5 Pacific Whale Foundation, Wailuku, Hawai‘i 96793, United States of America
6 The Charles Perkins Centre and Faculty of Veterinary Science, School of Biological Sciences, University of
Sydney, Sydney, Australia
Corresponding author email: * email@example.com and ** firstname.lastname@example.org
Author contributions: KAS collected part of dataset, MS, MD, EM and GEM-C analysed the data. MS, MD, KAS,
EM, EB and GEM-C wrote and edited the manuscript.
Key Words: seabirds, climate variability, apex predators, Hauraki Gulf, New Zealand
Few studies have investigated regional and natural climate variability on seabird populations using ocean reanalysis
datasets (e.g. Simple Ocean Data Assimilation (SODA)) that integrate atmospheric information to supplement ocean
observations and provide improved estimates of ocean conditions. Herein we use a non-systematic dataset on
Australasian gannets (Morus serrator) from 2001-2009 to identify potential connections between Gannet Sightings
Per Unit Effort (GSPUE) and climate and oceanographic variability in a region of known importance for breeding
seabirds, the Hauraki Gulf (HG), New Zealand. While no statistically significant relationships between GSPUE and
global climate indices were determined, there was significant correlation between GSPUE and regional SST
anomaly for HG. Also, there appears to be strong link between global climate indices and regional climate in the
HG. Further, based on cross-correlation function coefficients and lagged multiple regression models, we identified
potential leading and lagging climate variables, and climate variables but with limited predictive capacity in
forecasting future GSPUE. Despite significant inter-annual variability and marginally cooler SSTs since 2001,
gannet sightings appear to be increasing. We hypothesize that at present underlying physical changes in the marine
ecosystem may be insufficient to affect supply of preferred gannet main prey (pilchard Sardinops spp.), which
tolerate a wide thermal range. Our study showcases the potential scientific value of lengthy non-systematic data
streams and when designed properly (i.e., contain abundance, flock size, and spatial data), can yield useful
information in climate impact studies on seabirds and other marine fauna. Such information can be invaluable for
enhancing conservation measures for protected species in fiscally constrained research environments.
Many studies have explored quantitative and qualitative relationships between ecosystem state and seabird
vital rates and life history parameters within the context of climate change impacts (Dann et al. 2003; Mills et al.
2008; Ainley and Blight 2009; Chambers et al. 2011), predominantly natural climatic fluctuations. Seabirds are
subject to the vagaries of both terrestrial and oceanic changes as they breed on land but spend the majority of their
lives at sea (Lack 1968). These long-lived marine apex predators are well known ―biomonitor species‖, offering
opportunities to detect and assess the biological effects of changes in physical parameters (sea surface temperature -
SST, salinity, depth of thermocline and environmental oscillations) of the marine ecosystem (Schreiber and
Schreiber 1984; Furness and Camphuysen 1997).
To date, research into climate impacts on marine ecosystems tend to focus on biogeochemical and lower
trophic effects (Ito et al. 2010; Doney et al. 2012). However, this is changing with recent studies now involving an
understanding climate variability effects on top marine predators, as reviewed in Hobday et al. (2013) and
ecosystem forecasting and downscaled modelling in fisheries ecology (Hollowed et al. 2013). There is also a wealth
of literature on climate impacts on seabird reproductive biology and population characteristics (reviewed in
Sydeman et al. 2012), although, studies in waters around Australia and New Zealand in the south-western Pacific are
limited (Chambers et al. 2011).
Previous studies on seabirds indicate that they adapt variably to climate change — the effects are often
dictated by intrinsic life history factors and indirectly, via increases or decreases in SST and other climatological
factors (Chambers et al. 2012, 2013; Quillfeldt and Masello 2013). Although seabird responses to environmental
change are difficult to predict, there are certain consistent patterns (e.g. changes in distribution, phenology) that do
enhance our ability to understand and forecast potential population level consequences in different geographic
The need for inter-decadal time series data has created impetus to establish target species or ecosystem-
specific studies. However, such studies are not always possible with depreciating research capacity and budgets
worldwide. Thus, alternative data sources need to be considered while acknowledging the limitations associated
with these datasets.
Information gleaned from examining historic and current climate trends and purported correlation with
species distribution or occurrence patterns, and other demographic parameters can help shape how future studies
involving systematic and non-systematic data collection are structured and what parameters are influential. This is
especially true for mobile species such as seabirds (Bunce et al. 2002) and marine mammals (Ballance et al. 2006)
that feed at the top of the food chain, but are part of a complex food network that generally preclude direct
correlations with physical and biological changes. However, they can respond to some systemic changes more
strongly than others.
We consider a survey to be systematic if data was collected from a randomized study design with an equal
probability of sampling all points in the study area, e.g., line-transect boat or aerial surveys (Buckland et al. 2012).
Whereas, we consider non-systematic surveys to be data collected opportunistically from a boat or aircraft providing
a reasonable coverage of the study area and similar methods of data collection.
The Hauraki Gulf (HG) North Island, New Zealand (Fig. 1) is recognized for its cultural, economic and
ecological significance as a Marine Park (Hauraki Gulf Marine Park Act (2000), Parliamentary Counsel Office,
Wellington, New Zealand). The HG encompass an area of ca. 4,000 sq km, is a shallow (maximum water depth ~60
m), semi-enclosed body of water riddled with islands and shallow reefs that extend into waters of the western Pacific
Ocean. Water circulation in the region is primarily driven by tides and wind (Heath 1985; Zeldis et al. 2004; Gaskin
and Rayner 2013). Most of the HG area is also recognized as an ‗Important Bird Area‘ (IBA) by New Zealand
Forest and Bird (http://www.forestandbird.org.nz/), an affiliate of Birdlife International (Gaskin and Rayner 2013).
This region is a breeding spot for one of the most successful seabirds in New Zealand, the Australasian
gannet (Morus serrator; hereafter gannets). Gannets feed mainly on pelagic fish and squid (Robertson 1992;
Machovsky-Capuska et al. 2011a; Schuckard et al. 2012; Tait et al. 2014). These highly specialized marine
predators have been reported to travel for food as far as 388.5 km (Machovsky-Capuska et al. 2013a, 2014) with the
ability to assess prey density to increase foraging success (Machovsky-Capuska et al. 2013b). Their populations
have been increasing since the 1980s around New Zealand and the 1990s in Australia (Bunce et al. 2002). Currently,
there are 29 gannet colonies in New Zealand, three located on the east coast and 26 on the west coast, with an
estimated total of 48,509 pairs based on a census in 2000 (Nelson 2005), and an annual mean population growth rate
of 2.3 % (Robertson 1992).
The HG is home to four breeding colonies: Horuhoru Island, Mahuki Island, Motukaramarama, and
Motutakupu (Wingham 1985), with an estimated population of 12,726 pairs according to the 1980/81 census
(Wodzicki et al. 1984) — no recent counts are available. It appears that gannet populations may be robust in the HG;
however, with increasing human impacts and changing oceanographic conditions, it is unknown how these changes
are permeating into the ecosystem and affecting gannet populations (Gaskin and Rayner 2013). For example, the HG
supports a highly profitable fishery for snapper (Pagrus spp.) as well marine farming. In fact, New Zealand‘s largest
marine farms are in Firth of Thames, located in the southern sector of the HG (Aquaculture New Zealand 2010).
Increases in gannet populations off New Zealand and Australia have been attributed to warming SST,
increased El Niño Southern Oscillation (ENSO) activity and associated with increased (preferred) prey availability,
i.e. pilchard (Sardinops sagax) (Bunce et al. 2002). The effect of expanding inshore commercial fishing activity in
New Zealand leading to a greater presence of surface-schooling fish, normally preyed on by commercial species,
may also impact gannet foraging behavior (Robertson 1992; Schuckard et al. 2012). However, such overlap with
commercial fisheries also raises the risk of gannet mortality via gear entanglements (Norman 2000).
In this study, we use a dataset collected during non-systematic surveys, to explore potential linkages
between regional and global climate variability and observed inter-annual fluctuations in gannet sightings in the HG.
Specifically, we examine how regional variability correlates with global climate processes (e.g. ENSO), and with
gannet sightings. In addition, we provide preliminary results about leading and lagging climate variables and climate
predictor variables that could potentially influence gannet populations in the HG. This study showcases the potential
scientific value that non-systematic long-term datasets can provide, if appropriately employed, to fill regional data
gaps in resource constrained settings.
To examine relationships between 12-year observations of gannets in the HG, New Zealand (36.3° S,
175.08° E) and regional oceanographic and climate variables, we used the Simple Ocean Data Assimilation (SODA)
reanalysis product (Version 2.0.2-4) (Carton et al. 2000a, b; Tillinger and Gordon 2010). We acquired total and
anomaly data (i.e. departure of observed conditions from average conditions in that region) for the following
variables from 1990-2012: monthly and annual SST, zonal (west-east) and meridional (south-north) wind stress
(horizontal force of the wind at the surface of the ocean) and velocity, and cube of wind speed (a measure of water
turbulence and mixing in surface waters).
The SODA analysis is derived from the global circulation model that uses the Geophysical Fluid Dynamics
Laboratory Modular Ocean Model (Version 2.b) (Carton et al. 2000a, b). Data is derived over a rectangular grid
covering our region of interest (i.e. HG). SODA data are stored on a 0.5 x 0.5 degree grid with a resolution at 36° S
to be approximately 44 km by 55 km. The chlorophyll data was obtained from NASA‘s MODIS mapped, 'monthly'
data on 9 km or 1/12 degree global grid (http://oceandata.sci.gsfc.nasa.gov/), available from 2003-2012 for the HG.
Gannet data were collected across all austral seasons between 2001 and 2009 on board Dolphin Explorer
(DE), a 20 m tour catamaran powered by twin 350 horse-power inboard diesel engines, with a 5 m elevated
observation platform. Due to permit conditions, tours conducted by DE in the Hauraki Gulf were restricted to waters
south of a line from Cape Rodney to Great Barrier Island and to Cape Coleville on the Coromandel Peninsula (Fig.
1); and in water depths > 10 m. Data presented here represent all months of each year with the exception of August-
September 2002 and January-May 2007, when the vessel was on dry dock. While surveys were non-systematic,
survey routes selected were based on prevailing weather and sea conditions, with an attempt made to cover regions
not previously surveyed within that month in on order to facilitate on-board research focusing on common dolphin
(Delphinus sp, Stockin et al. 2008b;2009) and Bryde‘s whales (Balaenoptera edeni, Wiseman et al. 2011).
Observations were undertaken by experienced observers using a continuous scan sampling methodology
(Hoffman et al. 1981) visually with or without binoculars (Bushnell 10 x 50 magnifications). Sighting cues used to
detect cetaceans included splashing and/or water disturbance due to activity of animals, including gannets. Once
within 400 m of a group of gannets and other seabirds, environmental variables were recorded (water depth, SST,
tidal and sea state, visibility, wind direction and speed). Time and location of gannet sightings were noted using a
Global Positioning System (GPS). Data included in the analysis were limited to good visibility (≥ 1 k m) and
Beaufort Sea State < 4.
Gannet sightings data were analysed for 2001-2009 (Fig. 2) and included data for 99 months. The number
of individual gannets observed per sighting or flock size was not recorded. To normalize sightings information
across months and years, a sighting per unit effort (SPUE) was calculated, where:
We used monthly GSPUE to correlate with mean monthly climate and oceanographic SODA variables
across years (2001-2009). Gannets are commonly found in multi-species foraging aggregations. These aggregations
can include common dolphins, Bryde‘s whales, shearwaters and terns, kahawai (Arripis spp.), jack mackerel
(Trachurus spp.), snapper (Pagrus spp.) and hammerhead sharks (Sphyrna spp.). The frequency of occurrence of
these multispecies associations is reflective of prey abundance (Stockin et al. 2008, 2009; Machovsky-Capuska et al.
2011a). As such, we calculated an annual Multi-species SPUE (MSPUE), which is the occurrence of multi-species
aggregations during gannet sightings and correlated the MSPUE with annual mean climate and oceanographic
SODA parameters for HG.
To explore the influence of global climate processes via teleconnections on regional climate dynamics, we
investigated ENSO and regional SST pattern correlations over a longer time series (1990-2012). We used Southern
Oscillation Index (SOI) total and anomaly data from the Bureau of Meteorology, Australia
(http://www.bom.gov.au/climate/), and Niño 3.4 (equatorial Pacific 170W – 120W, 5S – 5N ) and Niño 4 (equatorial
and central Pacific 160E – 150W, 5S – 5N) region data, available through the Climate Prediction Centre, National
Centers for Environmental Prediction, USA (NCEP; http://www.cpc.ncep.noaa.gov/data/). These ―Niño boxes‖ are
typically used in the diagnosis and forecast of El Niño.
We also examined a potential association with the Southern Annular Mode (SAM) or Antarctic Oscillation,
which is the westerly wind belt around Antarctica with strong influence on large-scale variability in atmospheric
circulation in the Southern Hemisphere and describes large-scale alternations of atmospheric mass between the mid-
and high latitudes. Typically, negative SAM is associated with El Niño events (Wang and Cai 2013). On a shorter
time-scale (2001-2009), we also tested for correlations between GSPUE (2001-2009) data with the global climate
indices. We then conducted sample cross-correlations and ordinary linear lagged regressions of the climate and
gannet time series data in R (R Core Team 2014), to identify significant linear relationships between our time series
of interest, and identify potential climate variables that might be useful predictors of GSPUE. We used complete
case analysis to deal with GSPUE missing values (n=9). The time series were examined for significant evidence of
non-zero correlations for lags 1-20 months using the Ljung-Box test in R. Climate data were lagged by one month
and tested against GSPUE data (2001-2009). Maximum lags (k) were automatically limited to one less than the
number of observations in the series, but were also evaluated based on Cross-Correlation Function (CCF) lag plots.
Since we were primarily interested in identifying ―leading‖ climate variables to help predict future values of
GSPUE, we focused on the negative k spectrum.
We ran parametric tests as data were normally distributed with constant variance. The GSPUE was
significantly different across years (ANOVA, F = 7.580, n = 99, df = 8, P < 0.0001, but not across months within
each year (ANOVA, F = 0.651, n = 99, df = 11, P = 0.781, α = 0.05). In general, an increase in gannet observations
corresponded to an overall decrease in HG annual mean SST (total) from ca. 18.2 to 17.4 °C (Fig. 3a). Although the
mean annual SST has remained stable, ranging from 17.5 °C in 1990 to 17.7 °C in 2012, seasonal SST anomalies in
HG indicate an overall cooler period in the 1990s, with a slightly warmer period after 1998, followed by a decline
and plateau (Fig. 3b). This trend is also reflected in the overall variability in SST anomaly in the HG since 1990,
which is negatively correlated with SOI Anomaly (Fig. 3c).
Pearson‘s correlations of GSPUE and SODA climate and oceanographic variables resulted in a significant
correlation with only SST anomaly (Pearson‘s correlation r=-0.226, n=99, p<0.05). Annual correlations between
MSPUE and all annually averaged SODA variables were statistically non-significant . For wind velocities and
stress, values greater than zero indicate northerly winds in the meridional direction (S-N) and values greater than
zero indicate easterly winds in the zonal direction (W-E). Overall, during the period of interest, 3-year moving
averages suggest that winds tended to be more north and eastwards in 2004, south and eastwards between 2005 and
2007, and north and westwards since 2007 (Fig. 4). In general, wind cube and chlorophyll anomaly data, based on 3-
year moving averages, show a decrease after 2006-2007 (Fig. 4).
We found no significant correlations between GSPUE and the global climate indices. Over a longer time
frame (1990-2012), there are clear and significant relationships between regional SST in the HG and all global
climate indices tested. SSTHG (total and anomalies) were positively correlated with SAM and SOI and predictably,
negatively correlated with Niño 3.4 and 4.0 indices (Appendix).
Niño 3.4 and SOI variability suggests that in general 2009-2010, 2006-2007, 2002-2003 were associated
with weak to moderate El Niño periods and weak SOI with the strongest ENSO activity during 1997-1998 and
1994-1995. In contrast, La Niña was strong during 2010-2012 and generally moderate to weak historically for the
time period considered (Source: Bureau of Meteorology, Australia ―ENSO wrap up‖
http://www.bom.gov.au/climate/enso/). Generally, sustained periods of negative (positive) SOI are associated with
warm (cold) ocean waters corresponding to El Niño (La Niña) episodes (Fig. 3c).
Since the time series satisfied the properties for a stationary series (autocorrelation for a specific lag is same
throughout the time series), we conducted sample CCF and lagged regressions. For a specific lagt+k (t = time, k =
number of lags), negative correlations tend to imply that the predictor variable ‗leads‘ the dependent variable of
interest, and positive values could suggest that climate variables ‗lag‘ the outcome variable. For example, in Fig. 5,
the negative correlations for meridional wind velocity (total and anomaly) for negative k, imply that these variables
‗lead‘ GSPUE. Whereas, positive correlations of cube of wind speed (total and anomalies), SOI anomaly and SAM
suggest that these could ‗lag‘ GSPUE (Fig. 5). Dominant correlations corresponded to coefficients of ±0.15 or
higher depending on the variable of interest. Lags with high correlation coefficients were then tested further with
lagged linear regressions (variables with significant model outputs are shown in Table 1).
Based on scatter plots of climate variable (xt) and their lags (xt-1) and Partial Autocorrelation Function
(PACF) residuals, we determined that an autoregressive model of order 1 or AR (1) would be applicable, which is a
linear association of the current value of the time series on the previous value (t-1). Among climate variables tested
meridional wind velocity model coefficients were significant for lags, 2, 8, and 14 and lag 2 and 14 based on
meridional wind velocity anomaly coefficients.
Cube of wind speed total and anomaly coefficients were significant for lags 3, 7, and 9 and lags 2, 3, 7, and
9, respectively. Similarly, lag 5 and 11 for SAM and lag 2 for SOI anomaly resulted in significant coefficients
(Table 1). However, R2 values were weak (ranging from 7 % to a maximum of 16 %, Table 1). Autocorrelation and
Partial Autocorrelation Functions (ACF and PACF) of the residuals indicate minor autocorrelation.
Changes in ecosystem condition manifest themselves through physical changes that directly and indirectly
affect trophic relationships and species abundance (Stenseth et al. 2002; Mills et al. 2008; Burthe et al. 2012;
Sydeman et al. 2012). Alteration of ocean characteristics due to anthropogenic climate change is now indisputable
(IPCC 2013), but how these fluctuations translate into impacts on marine organisms at different spatial and temporal
scales is less understood (Ballance et al. 2006).
Our analysis indicates that inter-annual variability in gannet sightings was significantly and negatively
correlated with SST anomaly in HG. Lack of direct correlation with other variables tested could be due to, a) our
inability to detect a relationship due to data limitations, b) as flexible foragers (Schuckard et al. 2012), gannets
have successfully adapted to variable climatic conditions, or c)there may be a delayed reaction to systemic changes
(Doney et al. 2012). Despite the slightly cooler SSTs, we suggest that oceanographic conditions offered ideal
foraging conditions for increased gannet observations during the period of study. This is supported in part by the
spatial distribution of gannets in the study area with observations dominant around 36.5 and 37 degrees S latitude
consistently since 2001 (Fig. 6).
Although we did not see any significant relationship between GSPUE and global climate indices,
particularly the Southern Oscillation Index – SOI as evidenced in previous works (Bunce et al. 2002; Stenseth et al.
2002), lagged regression models suggest that the cube of wind speed, meridional wind velocity, SAM, and SOI
could be influential factors governing gannet patterns in HG, but since they individually explain minimal variation
in gannet observations, their collective and individual predictive value requires validation. In spite of the minimum
variability explained by these variables, we know from other studies, that wind speed and direction can have an
effect on foraging efficiency and breeding success, e.g., wandering albatross, Diomedea exulans, in the Southern
Ocean (Weimerskirch et al. 2012). Weimerskirch et al. (2012) found that the meridional component was a major
driver for increase in flight speed during foraging trips for wandering albatross. Similarly, Amélineau et al. (2014)
noted that wind force and direction could affect foraging costs in northern gannets, M. bassanus. In New Zealand,
negative phases of the SAM are associated with increased westerlies and lighter winds during the positive phases of
SAM (Renwick and Thompson 2006). Thus, each of the predictor variables identified here could have potentially
important effects on foraging and breeding success for gannets, and merit further investigation.
Environmental variables can also operate in an additive fashion, constructing Generalized Additive Models
(GAMs) using various climate and other (e.g. year and month) parameter combinations may help explain gannet
variability. To avoid overstating results and to discount spurious correlations, we emphasize that such models should
be built using robust gannet datasets and population metrics such as flock size or abundance. Opportunistic surveys,
if designed to collect these additional metrics or used in combination with systematic survey data, will likely
strengthen preliminary conclusions and reduce uncertainty associated with such datasets.
The strong relationships between regional SST and global climate indices found here suggests that global
climate variability could affect the climate seascape of HG, but not necessarily translate into observable changes in
upper-trophic predators based on current evidence. Changes in SOI can alter wind strength and direction, nutrient
upwelling and SSTs (Trenberth and Shea 1987; Bunce et al. 2002). As such in the HG, there are seasonal shifts in
wind patterns which fluctuate during El Niño and La Niña episodes such that westerlies (winds from the west) are
typically associated with El Niño periods and upwelling (Broekhuizen et al. 2002). SST patterns have been steady
over a 20-year period, but cooler than average in the region during the time frame of the analysis. Marginal changes
in thermal regimes can have profound effects on seabird populations as evidenced in the northwest Atlantic
(Montevecchi and Myers 1997), however, it remains to be seen if gannets in HG will be affected if a cooler regime
continues. In HG, similar to trends observed nationally in Australia and New Zealand (Bunce et al. 2002), gannets
populations may be stable and increasing. Variable wind patterns and ENSO periods notwithstanding in HG, suggest
it is possible that food is readily available and plentiful for them to remain in the area. Nonetheless, independent and
current census counts are necessary to contextualize a likely increasing trend in gannet populations in HG.
In terms of the prey field, New Zealand gannet diet is composed of pelagic fish and squid species,
predominantly, pilchard (Sardina spp.), anchovy (Engraulis spp.), saury (Scomberesox spp.), jack mackerel, squid
(Nototodarus spp.) and garfish (Belone spp.) (Wingham 1985; Robertson 1992; Machovsky-Capuska et al. 2011a, b;
Schuckard 2012). Anchovies are abundant throughout the Hauraki Gulf during spring and migrate seaward in winter
(Paulin et al. 1989). Peak abundance occurs in winter with high productivity leading to large schools of anchovies
often closely associated with pilchards, which are abundant during warmer, less productive periods (Lecomte et al.
Pilchards are susceptible to viral disease and are sensitive to climatically driven oceanographic conditions
(Whittington et al. 1997; Paul et al. 2001). Nonetheless, pilchards accounted for more than 50 % of the diet of
gannets at Port Phillip Bay between 1995-1998 (Bunce et al. 2002), 90 % of the diet of gannets at Farewell Spit
between 1995-2001, excluding 1996 that switched to anchovy due to a mass pilchard mortality (Shuckard et al.
2012), and were the most abundant prey species found in the diet of gannets in the HG between 1979-1980
(Wingham 1985; Robertson 1992). The absence of pilchards as a primary food source is attributed to be a major
factor for the biggest crash in gannet populations ever recorded in New Zealand including at the Farewell Spit
colony where hundreds of birds were found dead in 1996 (Schuckard et al. 2012).
Pilchards (also known as sardines) have a preference for warm waters (Neumann 2001; Chavez et al.
2003), but remain tolerant to a wide range of temperatures, with spawning occurring between 13.5 and 25 ºC as
determined in Pacific sardines (Lluch-Belda et al. 1992). Thus, the marginally cooler SSTs recorded in the HG
region may still be optimum for pilchard spawning, although recruitment and productivity levels in these waters are
unknown. The East Auckland Current and shelf upwelling, along with tidal changes drive circulation patterns
interact to affect nutrient production in the Gulf, which is further influenced by ENSO patterns and Interdecadal
Pacific Oscillations (IPO) (Zeldis et al. 2004). So, lack of upwelling may have an impact on pilchard/anchovy
populations, with further investigations necessary to see how prey availability is linked with gannet population
The advantages of opportunistic datasets are that they are continuous both in terms of effort and temporal
scales particularly involving commercial operations. The standalone scientific value of these datasets cannot be
discounted. For example, Williams et al. (2006) demonstrated that it is possible to estimate marine mammal
abundance from non-randomized opportunistic surveys and be potentially used in management decision-making.
Fisheries observers on commercial fishing operations in the USA are one of the primary sources of marine mammal
by-catch information, which is utilized in marine mammal stock assessments by the US National Marine Fisheries
Service (http://www.nmfs.noaa.gov/pr/sars/). Conversely, exclusive reliance on opportunistic data can lead to
erroneous conclusions about abundance and distribution patterns due to incomplete coverage of study area and
selective or improper data gathering.
Nevertheless, in areas where research efforts are limited or where adequate funding is lacking, we propose
that long-term non-systematic survey results, if properly collected and analyzed, can be a valuable tool to address a
variety of marine conservation problems, including discerning probable effects of regional and global climate
variability on living marine resources.
In the present study, our interpretation and analysis would be significantly improved by including key
metrics such as abundance and presence/absence information. Synthesis studies involving analysis of multiple
species and their response to climate or oceanographic change would enhance our understanding of climate impacts
within ecosystems. We further need to understand the interactive effects of climate-ecosystem variables and human
impacts on seabird population dynamics to be able to forecast with increased certainty population responses to
climate change (Jenouvrier 2013). Also, since seabirds are significant ecological links between the land and the sea,
changes in breeding sites and gannet vital rates are important considerations. Future studies should use multiple data
streams from both systematic and non-systematic work to better understand and predict gannet responsiveness to
oceanographic and climate variability at regional scales.
We acknowledge the management and the crew of Dolphin Explorer, Auckland Whale and Dolphin Safaris
and the New Zealand Department of Conservation for providing an opportunistic observation platform and for
making accessible historical datasets on request. We thank E. Libby for helpful comments on early versions of the
manuscript and acknowledge R. Murtugudde (Earth System Science Interdisciplinary Centre, ESSIC, University of
Maryland) and Jim Beuchamp (ESSIC) for useful reviews that greatly improved the manuscript. Special thanks to J.
Beauchamp (ESSIC) for acquisition of SODA and MODIS datasets. Aspects of this research were funded by the
Massey University Research Fund (MURF). Special thanks to anonymous reviewers whose suggestions greatly
improved the manuscript.
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Table 1. Lagged multiple regression model outputs wherein GSPUE is assumed to be linear function of historic lags
(in months) for the following climate variables with significant coefficients: Wcube = cube of wind speed,
WcubeAno = cube of wind speed anomaly, Mvel = meridional wind velocity, SOIAno = Southern Oscillation Index
anomaly, and SAM =Southern Annular Mode
Model = GSPUE ~ SOIAnolag1 + SOIAnolag2 + SOIAnolag3 + SOIAnolag5
Coeffici ents: Coeffi ci ents:
Estimate Std. Error t value Pr(>|t|) Estimate Std. Error t value Pr(>|t|)
Wcubelag3 0.0005 0.0002 2.223 0.02 9 * SOIAnol ag1 0.019 0.02 70 0.71 0 0.480
Wcubelag7 0.0005 0.0003 2.064 0.04 21 * SOI Anola g2 0.057 0.0280 2.086 0.040 *
Wcubelag9 0.0008 0.0003 2.766 0.00 7 ** SOIAnol ag3 0.028 0.02 93 0.96 3 0.339
*** 0.001 ** 0.01 * 0.05 SOIAnol ag5 0.001 0.02 8 0.038 0.970
Multi pl e R-squa red=0.15 Adjusted R-squared=0.12 *** 0.001 * 0.05
F = 5.197 on 3 and 86 df, p = 0.002 Multi pl e R-squa red=0.12, Adjus ted R-squa red=0.08
F= 3.108 on 4 and 89 df, p = 0.019
Model = GSPUE ~ SAMlag5 + SAMlag7
Coeffici ents: Coeffi ci ents:
Estimate Std. Error t value Pr(>|t|) Estimate Std. Error t value Pr(>|t|)
WcubeAnola g2 0.0004 0.0003 1.263 0.210 SAMla g5 0.071 0.035 2.048 0.044 *
WcubeAnola g3 0.0006 0.0003 1.979 0.051 SAMlag7 0.060 0.036 1.669 0.099
WcubeAnolag7 0 .0007 0.0003 2.255 0.027 * *** 0.001 * 0.05
WcubeAnolag9 0 .0008 0.0003 2.483 0.015 * Multi pl e R-squa red= 0.09, Adjus ted R-squa red=.07
*** 0.001 * 0.05 F = 4.172 on 2 and 89 df, p = 0.019
Multi pl e R-squa red=0.19, Adjus ted R-squa red=0.15
F= 4.999 on 4 and 85 df, p = 0.001
Model = GSPUE ~ MvelAnolag2 + MvelAnolag14
Coeffici ents: Coeffi ci ents:
Estimate Std. Error t value Pr(>|t|) Estimate Std. Error t value Pr(>|t|)
Mvellag2 -0.07 0 0.027 -2.583 0 .0116 * MvelAnol ag2 -0.092 0.02 9 -3.258 0.002**
Mvellag8 -0.04 3 0.026 -1.687 0 .096 MvelAnola g14 -0.097 0.028 -3.527 0.001 ***
Mvellag14 -0 .075 0.02 7 -2.834 0.006 ** *** 0.001 ** 0.01
*** 0.001 ** 0.01 * 0.05
Multi pl e R-squa red= 0.15, Adjus ted R-squa red=0.12 Multi pl e R-squa red= 0.17, Adjus ted R-squa red=0.16
F= 4.885 on 3 and 81 df, p = 0.004 F= 8.835 on 2 and 82 df,p=0.000 3
Model: GSPUE ~ Wcubelag3 + Wcubelag7 + Wcubelag9
Model: GSPUE ~ Mvellag2 + Mvellag8 + Mvellag14
Model = GSPUE ~ WcubeAnolag2 + WcubeAnolag3 +
WcubeAnolag7 + WcubeAnolag9
We identified links between Gannet Sightings Per Unit Effort (GSPUE) and climate variability.
GSPUE was linked with regional Sea Surface Temperature anomaly and regional climate indices.
We identified potential climate variables with capacity to forecast future GSPUE.
Despite inter-annual variability on SSTs gannet sightings appeared to be increasing.
We demonstrate the scientific value of non-systematic data in climate impact studies on seabirds.