Content uploaded by Nyree Fea
Author content
All content in this area was uploaded by Nyree Fea on Jan 28, 2021
Content may be subject to copyright.
Available via license: CC BY-NC
Content may be subject to copyright.
VOLUME 13, ISSUE 2, ARTICLE 11
Fea, N. and S. Hartley 2018. The balancing act of nest survival: survival of a small endemic bird in the face of ship rat predation and other risk
factors. Avian Conservation and Ecology 13(2):11. https://doi.org/10.5751/ACE-01284-130211
Copyright © 2018 by the author(s). Published here under license by the Resilience Alliance.
Research Paper
The balancing act of nest survival: survival of a small endemic bird in
the face of ship rat predation and other risk factors
Nyree Fea 1 and Stephen Hartley 1
1Centre for Biodiversity and Restoration Ecology, School of Biological Sciences, Victoria University of Wellington
ABSTRACT. Predation of indigenous birds by ship rats (Rattus rattus, [Muridae]) is an international conservation crisis and has been
implicated in the decline of many endemic species. Effective management of threatened ecosystems relies on accurate assessments of
invasive species impacts on native wildlife. To quantify the link between ship rat abundance and survival of small, endemic birds we
investigated the prevalence of rat predation on nesting New Zealand Fantails (Rhipidura fuliginosa placabilis, [Rhipiduridae]), and its
importance relative to other risk factors such as nest microsite. We surveyed 106 nests across forested reserves in Wellington City, New
Zealand. Local abundance of ship rats was indexed using chew-cards placed around the nest and with tracking tunnels throughout
reserves. We modeled the effects of ship rat abundance, weather, observer impact, and attributes of the nest for their influence on nest
survival. Fantails were more likely to abandon nests located higher in trees and those built earlier in the breeding season. More nests
failed when rat abundance was higher. Where ship rat abundance reached a 25% chew-card index (CCI), the probability of the nest
surviving dropped below 50%, and for CCI above 45% only 20% of nests were predicted to survive. However, Fantails also exhibited
a resilient strategy that improved survival because nests located on thinner branches were less likely to suffer predation. Our research
suggests that nesting strategies of Fantails involve trade-offs and strategies that might protect them against one threat, might expose
them to others. Fantails are a common endemic species and cope with moderate levels of nest predation, however conservation of small
endemic birds with less resilient breeding strategies is likely to require management of ship rat populations to low levels.
L'art du compromis de la survie du nid : la survie d'un petit oiseau endémique face à la prédation par le
rat noir et d'autres facteurs de risque
RÉSUMÉ. Enjeu de conservation d'ampleur internationale, la prédation d'oiseaux indigènes par le rat noir (Rattus rattus, [Muridae])
est associée à la diminution de nombreuses espèces endémiques. Le succès de la gestion d'écosystèmes menacés dépend de l'évaluation
précise des impacts d'espèces envahissantes sur la faune indigène. Pour quantifier la relation entre l'abondance de rats noirs et la survie
de petits oiseaux endémiques, nous avons examiné la fréquence de prédation du Rhipidure à collier (Rhipidura fuliginosa placabilis,
[Rhipiduridae]) nicheur par le rat, et son importance relative par rapport aux autres facteurs de risque tels que le microsite du nid.
Nous avons suivi 106 nids dans des réserves boisées dans la ville de Wellington, en Nouvelle-Zélande. L'abondance locale des rats noirs
a été évaluée au moyen de cartes à morsures placées autour des nids et de tunnels de suivi dans les réserves. Nous avons modélisé l'effet
de l'abondance du rat noir, des conditions météorologiques, de l'impact de l'observateur et des attributs du nid pour déterminer leur
influence sur la survie du nid. Les Rhipidures à collier avaient tendance à abandonner leur nid quand celui-ci était placé haut dans un
arbre ou avait été construit tôt en saison. L'échec des nids était constaté plus souvent quand l'abondance des rats était élevée. Là où
l'abondance de rats noirs atteignait un indice de 25 % fondé sur les cartes à morsures (ICM), la probabilité de survie du nid dégringolait
sous 50 %, et pour un ICM au-dessus de 45 %, le modèle prédisait seulement 20 % de survie des nids. Toutefois, les rhipidures ont aussi
adopté une stratégie de résilience qui amélioraient la survie, parce que les nids placés sur des branches plus fines étaient moins susceptibles
de subir de la prédation. Notre recherche indique que les stratégies de nidification des rhipidures comportent des compromis, et qu'une
stratégie qui les protège d'une menace les expose peut-être à d'autres. Le rhipidure est une espèce endémique commune qui fait face à
un degré de prédation des nids moyen; cependant, dans le cas de la conservation de petits oiseaux endémiques moins résilients dans
leurs stratégies de nidification, les gestionnaires doivent vraisemblablement maintenir les populations de rats noirs à de bas niveaux.
Key Words: density impact function; fantail; introduced mammals; predation; Rattus
INTRODUCTION
Predation of indigenous birds by introduced, invasive mammals
is an internationally common and widespread problem
(Courchamp et al. 2003, Towns et al. 2006, Doherty et al. 2016).
Waves of avifaunal extinctions have repeatedly occurred on
oceanic islands after the arrival of humans and the introduction
of mammalian predators (Atkinson 1985, Blackburn et al. 2004)
because the bird populations are often limited in size and range
and they are particularly vulnerable to habitat loss and predation
by novel species (Johnson and Stattersfield 1990). Predation by
invasive mammals has been implicated in the decline of many
species of birds across the Pacific region (Robertson et al. 1994,
Address of Correspondent: Nyree Fea, School of Biological Sciences, Centre for Biodiversity and Restoration Ecology, Level 3, Te Toki Rata
Building, Victoria University of Wellington, Wellington 6012, nyree.fea@gmail.com
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
Vanderwerf and Smith 2002, Innes et al. 2010a, Shiels et al. 2014,
Doherty et al. 2016) with the loss of 50% of all breeding bird
species in New Zealand after the arrival of humans and the
introduction of mammalian predators and competitors
(Holdaway et al. 2001). The well-documented negative effects of
ship rats (Rattus rattus) on island birds has led conservation
managers across the Pacific to focus on reducing rat populations
in forests of high conservation value (Robertson et al. 1994, Innes
et al. 1999, Vanderwerf and Smith 2002, Gillies et al. 2003).
However, conservation projects across New Zealand have
reported full recovery of ship rat populations one to two years
after control has ceased (Innes et al. 1995, Sweetapple and Nugent
2007, Ruscoe et al. 2011, Griffiths and Barron 2016). Outside of
intensively managed areas, continual suppression of ship rat
populations is rarely achieved, and their persistence is of concern
for bird species susceptible to ship rat predation.
In a climate of constrained budgets, it is important to know the
relationship between management actions and conservation
outcomes such as breeding success or population recovery of
threatened species. Because management often has the proximal
aim of reducing the densities of pest species (Duron et al. 2017),
the ability to relate pest abundance to likely conservation
outcomes allows for more accurate cost-benefit analysis and
prioritization of resources. Such relationships when quantified
are known as ecological “density impact functions” (Norbury et
al. 2015).
Predation of birds by ship rats causes population decline for many
native bird species in New Zealand (Innes et al. 2010a). Nest
survival has been studied following control of invasive mammals,
with results suggesting the positive effects of control are nullified
with the rapid recovery of ship rat populations (Armstrong et al.
2006). These authors modeled survival parameters for the North
Island Robin (Petroica longipes), a medium-sized endemic
passerine (35 g average female body weight; Heather et al. 2015),
according to a range of ship rat abundance indices. Armstrong et
al. (2006) described a significant linear relationship characterized
by moderate nesting success when ship rat abundance was low (≤
5%) and extremely low nesting success at high ship rat abundance.
Because body size of prey relative to a predator is an important
factor influencing prey vulnerability (Cohen et al. 1993, Newton
1998) it would be useful to also quantify the relationship between
ship rat abundance and nest survival of small endemic birds (<
20 g adult mass). However, obtaining sufficiently large samples
of nest attempts by threatened bird species is likely to be limited
by the very nature of the subject’s rarity. Additionally, disrupting
the nesting attempts of rare species can have dire consequences.
Calculation of density impact functions for predation impacts on
common surrogate species provides an ethical approach with a
higher likelihood of sampling success, one that could be applied
as best-case scenarios for conservation managers for predicting
survival of threatened endemic species.
In addition to avoiding predation, nest survival and successful
fledging of chicks is also dependent on favorable abiotic factors
throughout the nesting period. Severe weather, e.g., extreme
temperatures, wind, or rainfall, can not only undermine the
structure of the nest and threaten nest young, but can also limit
the behavior of nesting adults through the energy-demanding
breeding season (Conrey et al. 2016). Human activity at a nest
potentially introduces further factors that may alter survival
(Richardson et al. 2009), therefore research should account for
weather and human effects alongside predation threats to
understand the relative importance of risk factors.
Nest height and concealment are often investigated in studies
assessing the influence of nest placement on nest predation, and
how this relates to the type of predator, whether it is an avian,
arboreal, or ground predator (Colombelli-Négrel and
Kleindorfer 2009). In a study of a small passerine (the Blackcap,
Sylvia atricapilla), Remeš (2005) showed that nest height affected
survival differently depending on whether the predators were
avian or rodents (mice and voles). Lower nests survived better
with respect to avian predation but worse against rodent
predation. They also found less avian predation on more
concealed nests with no effect of concealment on levels of rodent
predation. Higher nests of the endemic Yellowhead (Mohoua
ochrocephala) and Yellow-fronted Parakeet (Cyanoramphus
auriceps) in the south of New Zealand were less likely to be preyed
upon by stoats and ship rats (Elliott et al. 1996). In contrast to
these studies, van Heezik et al. (2008) studied the influence of nest
placement on survival of New Zealand Fantail (Rhipidura
fuliginosa fuliginosa, South Island subspecies) nests and recorded
increased nesting success for lower nests even though mammalian
predators (rats and possums) were the main group of predators
identified by tooth marks on artificial eggs. They concluded that
the higher nests, which were also more concealed, fared worse
because they were more exposed to the negative effects of
inclement weather.
In New Zealand, agile mammals, with arboreal abilities, now pose
the greatest threats to native birds. We therefore set out to
investigate an additional nest placement factor, the diameter of
the nest branch, a placement strategy that might limit the
approach of a climbing predator. This aspect of nest site has
received little attention in the literature, and we could find no
examples where this has been investigated for species targeted by
ship rats. Ship rats have been identified as major predators at
fantail nests (Mudge 2002, van Heezik et al. 2008) and these very
small birds might exhibit nesting adaptations that utilize the large
differences in weights between predator (average weight of an
adult ship rat in New Zealand, 146 g; King 2005) and prey (8 g
average female fantail body weight; Heather et al. 2015).
There is evidence that endemic bird species adapt nest placement
or other nesting strategies to respond to novel predatory threats
(Massaro et al. 2008, Vanderwerf 2012). Martin (1993) postulated
that nest-site selection could be evolutionary conserved, with
species in the same genus exhibiting similar nesting habits across
the various regions in which they occur. This implies that although
predatory mammals have not been present on New Zealand since
it separated from Gondwana 80 million years ago (Worthy and
Holdaway 2002), taxa that colonized more recently (such as the
New Zealand Fantail) may exhibit antimammalian predatory
responses that hark back to wider Australasian origins.
Conversely, birds with high-level endemism in New Zealand, may
be particularly vulnerable to novel, invasive mammalian
predators (Blackburn et al. 2004).
To investigate factors that might influence nest survival of a small,
endemic bird we measured the nesting success of the New Zealand
Fantail (Rhipidura fuliginosa placabilis, North Island subspecies),
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
across forest fragments in Wellington City. We chose to monitor
fantails because they are one of New Zealand’s smallest endemics
birds and are known to be attacked on the nest by ship rats (Mudge
2002). Although endemic, the New Zealand Fantail is closely
related to the Australian Grey Fantail (Nyári et al. 2009), and
therefore shares a fairly recent evolutionary history with native,
mammalian predators. Fantails are one of New Zealand’s smallest
birds and build small, light nests that could be placed beyond
reach of the larger ship rat. They are also a common and widely
distributed endemic species (Heather et al. 2015) and therefore
might possess resilient nesting strategies.
Our primary objective was to quantify threats to a common
endemic bird, including threats from the introduced ship rat.
Nests in fragmented habitat, such as urban forest reserves, are
vulnerable to ship rat predation because rats easily invade forests
from other quality habitat (Stirnemann et al. 2015) and long-term
suppression is therefore problematic (Innes et al. 2010b). The
forested reserves of Wellington provided an accessible site where
we were likely to encounter abundant ship rats and fantails. We
aimed to (1) derive a density impact function between an index
of rat abundance and an ecological outcome (nest survival of a
small passerine); (2) investigate the effects of weather and
observer presence on nest survival; and (3) determine the relative
influence of tree-height, nest height, nest branch-width, and nest
concealment on nest survival.
METHODS
Sites and seasons
We searched for nests in forested reserves in Wellington City, New
Zealand (41°S, 175°E; see Fig. A1.1 in Appendix 1, for a map of
the study area). The reserves are remnant patches of native forest
with hard edges bordered by urban areas and range in area from
2–50 hectares. The forests comprise broadleaf tree species with
scattered native podocarps and exotic pine trees (Gabites 1993).
We monitored nests between December 2014 and February 2015,
across four reserves in Wellington and again between August 2015
and February 2016, for a second, complete breeding season
throughout the city (20 reserves). In the second breeding season,
we adjusted our search effort to locate equal numbers of nests
across reserves with moderate, low, and zero rat abundance, with
rat abundance based on the first season’s tracking tunnels. Nests
from this last group (zero rats) were located in Zealandia, a fenced
eco-sanctuary in central Wellington from which introduced
mammals, except mice, have been eliminated (Empson and Fastier
2013).
Nest survival
We located nests by following adult fantails exhibiting nesting
behavior (collecting twigs from the ground or moss from trees,
catching an insect and flying off with it in their beaks). Once a
nest was sighted we ascertained its developmental stage by
observing behavior of the parents or by nestling plumage and
behavior (Amiot et al. 2015). We estimated the days for each phase
after clutch initiation as 3–6 days for laying, 13–16 days for
incubation, and 11–16 days with nestlings (Heather et al. 2015).
We attached one or two cameras (Bushnell HD trail cameras) to
trees or shrubs between 0.5 and 5.0 meters from the nest. No
cameras were used if the nest was visible from a public track.
Camera placement was delayed until after egg-laying to minimize
the risk of abandonment of early stage nests.
We visited nests approximately weekly to service cameras and to
record the nesting behavior of adults. Nests were observed from
a distance for up to 30 minutes. If there was no activity during
that time, we checked the nest contents either visually or using a
video camera mounted on an extendable tripod to determine nest
outcome. The contents of high nests were checked from a vantage
point with binoculars where possible. If the nest appeared
concluded, i.e., no action on the nest by adults and no live young
present, we then gathered evidence to determine the cause of the
nest’s outcome.
We categorized nests fully built but never receiving a clutch as
abandoned. Nests were regarded as abandoned on the first day
activity was observed to have ceased at the nest and where no eggs
were apparently laid. If nests appeared to be deserted by the
parents, and nest young were found dead in the nest, with no
evidence of predation, we assigned these nests a deserted status.
We used camera footage or other evidence of nest disturbance to
determine if nests spilled due to heavy rain or strong winds. Nest
predation was recorded if any one of the following criteria were
fulfilled: (1) the nest was empty before chicks could possibly have
fledged; (2) there was evidence of a predator, e.g., rat scat in the
nest; (3) egg or chick remains were found in or near the nest; (4)
camera evidence showed a predator at the nest site. Nest success
was confirmed if all of the following criteria were fulfilled: (1) the
nest was estimated to be at least 27 days postclutch initiation; (2)
chick droppings were found under the nest; (3) there was no
evidence of predator visitation from camera footage (if available).
Because nest success is defined by the survival of any nest young
to fledging (Mayfield 1975), we spent considerable effort
attempting to sight (and count) fledglings within one week of the
nest’s conclusion to conclusively determine the nest’s success.
We measured characteristics of the nest to investigate the
relationship between nest placement and nest survival. We
recorded the following nest attributes: tree height (m), nest height
(m), nest branch diameter (mm) at the point of attachment of the
nest to the branch, and percent foliar cover 2 m above and 2 m
below the nest using the foliage cover scale method commonly
used for assessing canopy cover (Department of Conservation
2014).
Rat relative abundance
We estimated ship rat abundance within reserves using two indices
of relative abundance. First, for both breeding seasons, we
deployed transects of tracking tunnels in reserves where multiple
nests, separated by > 100 m, had been discovered at building stage.
Tracking tunnel monitoring followed the protocols outlined by
Gillies and Williams (2013) with randomly located lines of 10
tunnels with 50 m spacing and baited with peanut butter. However,
as we were monitoring forest fragments, only 1–2 transects could
be placed within each reserve. Tunnels were run for one fine night
in February each year. Using the distinctive print impressions of
rats, we calculated percent tracked tunnels for that transect and,
where two transects were placed, we took the average. For two of
the reserves (Otari-Wilton’s and Johnsonville) we utilized tracking
tunnel data collected by Greater Wellington regional council in
February of both years as part of their routine monitoring.
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
Tracking tunnels are widely used across New Zealand forests for
estimations of relative abundance of rodents. Rat tracking rates
can predict biologically plausible growth rates of rat populations
(Elliott et al. 2018) and have been shown to reliably predict nest
success for threatened bird species (Innes et al. 1999, Armstrong
et al. 2006). For maximal correlation between rat abundance
indices to be upheld, Blackwell et al. (2002) highlighted the
importance of adhering to monitoring protocols such as those
prescribed by Gillies and Williams (2013). Blackwell et al. (2002)
also recommended that a second index of abundance be used
alongside tracking tunnels. An additional estimate was
particularly useful in our study, conducted within forest
fragments, where we were limited to only 1–2 transects per reserve
(rather than 4, the recommended minimum in Gillies and
Williams 2013).
During the second breeding season, we employed an additional
method to estimate the relative abundance of ship rats in the
vicinity of the nest. We set out chew-cards around concluded nests
(fledged or failed) to provide an estimate of abundance in the
immediate vicinity of the nest. Chew-cards were made of corflute
(Graley Plastics, Wellington), measured 90 mm x 180 mm x 3.3
mm, with internal flutes that we baited with peanut butter. Chew-
cards were deployed around nests, that had received a clutch, in
a 3 x 3 grid (min. 2 x 3 grid if access was limited on one border,
i.e., 6–9 cards) at 25 m spacing. To target the monitoring toward
the ship rat, we placed chew-cards at 1.4 m height, directly above
a tree-limb on trees with a diameter at 1.3 m height of > 25 mm.
Both ship rats and Norway rats (Rattus norvegicus) are present in
Wellington City forests (personal observation) however Norway
rats are reluctant climbers and therefore unlikely to prey upon
fantail nests (Foster et al. 2011). Chew-cards were deployed
around the concluded nest and were left in situ for one fine night,
as is prescribed for tracking tunnel monitors (Gillies and Williams
2013). Using the distinctive bite marks of ship rats, we calculated
percent chewed cards, i.e., the chew card index or CCI (Sweetapple
and Nugent 2011).
Analysis
Nest survival
To estimate survival of fantail nests, we calculated expected daily
survival rates (DSR) averaged across all nests that received eggs.
Survival rates were calculated for nests by breeding season and
separately for nests located across unfenced Wellington reserves
and within the fenced sanctuary, Zealandia, where all mammalian
predators, except mice (Mus musculus), have been removed. We
calculated maximum likelihood estimates of DSRs, and variance
over the entire nesting period, using the Nest Survival package
(Rotella 2015) in program Mark v6.2 (White and Burnham 1999).
The DSR is based on the Mayfield estimate (Mayfield 1975),
which accounts for the number of days the nest was active (from
clutch initiation) and the days the nest was not under observation.
We calculated nesting success (the probability of a nest surviving
from clutch initiation to fledged young) as the DSR raised to the
power of 31 because this is the average number of days for the
entire fantail nesting period (Heather et al. 2015).
We also explored time-dependent effects on the survival of nests
that received eggs. Survival may vary throughout the season
according to changes in abundance of predators or changes in the
conspicuousness of the nest as the nest young mature. We explored
these time dependent models in program Mark using (a) constant
survival across the season; (b) a linear-time model with variation
of nest-survival according to day of the season; (c) survival of
nests at early, middle, and late stages of the season; (d) survival
according to the nest phase (chick, nestling); and (e) survival
according to the nest’s age. We ranked these models in Mark using
AICc. We then included the highest ranked time-dependent
variable in the nest-site model ranking.
The assumptions of the nest survival model in program Mark are
the following: (1) that nest fates are correctly determined; (2) that
observer visits to the nest do not influence survival; (3) there is
no significant heterogeneity of daily nest survival rates; and (4)
that nest fates are independent (Dinsmore and Dinsmore 2007).
Violation of assumption (1) is unlikely because we were able to
make frequent nest checks, we employed intensive camera
monitoring, and we were able to closely monitor breeding pairs
to determine outcomes of nests. We also tested for the effects of
observer presence to determine if violation of assumption (2) had
occurred, as we modeled the effects of nest checks on nest
abandonment and the effects of camera placement on nest
predation. There is likely to be overdispersion in this data,
however, as a result of violations of assumption (3) and (4).
Currently there is no method to estimate extra-binomial variation
in program Mark (Dinsmore and Dinsmore 2007). We were able
to account for overdispersion in generalized linear mixed effect
models (GLMMs) of nest abandonment and nest predation, with
the addition of a random effect for fantail breeding pair. This
accounted for the nonindependence of nest outcomes where
multiple nests were recorded from a single breeding pair. Breeding
pairs were identified as the two adults attending to the nest, or
cluster of nests, which were all located within a 100-m radius.
Factors affecting nest abandonment
To investigate possible causes of nest abandonment we ran a
GLMM for all nests found at building stage with the binary fate
of the nest (abandoned or clutch laid) as the response variable.
We combined both 2014–2015 and 2015–2016 nests in this
analysis because the average life span of a New Zealand Fantail
is one year (Heather et al. 2015) and therefore pseudo-replication
of nest survival data from a breeding pair across consecutive
breeding seasons was unlikely. We also included a random effect
variable for fantail breeding pairs identified each season. We
specified a logit link function and binomial error structure. To
test for the influence of weather covariates (minimum temperature
[ºC], total rainfall [mm], and maximum wind gusts [m/sec] for the
seven days prior to conclusion) we sourced climate data from a
central Wellington weather station (Kelburn Station available at
http://cliflo.niwa.co.nz). We also investigated the effects of
season, using the day of the season the nest was observed
abandoned or laid in (counted from 28 August, the earliest day
of year that nesting was observed), plus year (2014–2015 or 2015–
2016) and nest-placement, i.e., branch width and nest height.
Finally, because we suspected that human visitation to the nest
would also be likely to cause disruption, we included a continuous
effect for the number of visits to the nest (before nest
abandonment/laying) for all nests found at building stage. To
exercise caution and minimize human presence at early stage nests,
we did not estimate rat abundance at nests unless they had received
a clutch and the nest attempt had been concluded.
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
We analyzed the influence of these factors on abandonment using
all factors in an additive, global GLMM in the multimodel
inference R-package MumIn, version 1.15.6 (Barton 2016). We
then ranked models representing all possible combinations of
factors (128 models total) using Akaike’s Information Criterion
for model selection, adjusted for small sample sizes (AICc;
Burnham and Anderson 2002). The Akaike weights of all models
in the set summed to one and the model with the highest weight
was accepted as most closely representing reality, i.e., the best fit
to the data at hand. Models were ranked by measuring the change
in AICc from the best model. Models with a change of < 2 from
the best model have substantial empirical support, models with
a change of 4–7 have considerably less support, and models with
a change of > 10 have essentially no support (Burnham and
Anderson 2002). To describe the relative importance of each
factor we calculated the sum of the Akaike weights, the beta-
estimate, and the beta standard error (SE) across all the models
in which it was present, i.e., 64 models each. Variables whose mean
beta is more than twice the magnitude of SE may be considered
significant from a hypothesis-testing perspective, even in the face
of uncertainty around model specification (Payton et al. 2003).
Rat density impact function
To estimate the relationship between rat abundance and nest
survival, we modelled nest survival in another GLMM as a
function of ship rat abundance, once using data from tracking
tunnels and a second time using chew-cards as our index of rat
abundance. Nests that were abandoned, spilled, or preyed upon
by birds were excluded from this calculation of a density impact
function. All other nests were included regardless of stage
discovered because the nest’s outcome in relation to the
abundance of ship rats was the relationship under investigation.
Following the methods outlined by Dinsmore and Dinsmore
(2007) we assumed that nest fates were correctly determined. We
analyzed the data in the lme4 R-package (Bates et al. 2015) with
a binary response variable (survived or failed) and rat abundance
as the explanatory variable. We included a random effect in the
model for the fantail breeding pair, and we specified a logit link
function and binomial error structure.
The effects of nest-site on nest predation
To estimate effects of nest placement on nest predation we
analyzed successful nests and those that failed to rat predation
and unknown predation using full model averaging of each nest-
site attribute, i.e., five variables, represented in 16 models each, in
program Mark. Nests that were abandoned, spilled, or preyed
upon by birds were excluded from this analysis. The nest-site
attributes were branch width, rodent abundance (using the nest-
site estimate from chew-cards), nest height, cover above, and cover
below. We included the highest-ranking time-dependent variable
in these models. We also tested for a possible effect of reduced
predation on nests with cameras using the one-sided binomial test
in the R base package, excluding nests that were spilled,
abandoned, or located in Zealandia.
Multicollinearity
We checked for multicollinearity between explanatory variables
using Spearman rank correlation tests in the R base package. If
a pair of variables had a correlation coefficient (rs) ≥ 0.7 we
excluded from analysis the variable shown less in the literature to
be relevant to nest survival. We used the Spearman rank
correlation coefficient, rather than the Pearson correlation
coefficient, because the former makes no assumptions about
linearity in the relationship between the two variables. The
variables nest height and tree height were strongly correlated (r =
0.87) as nests were generally located directly under the tree’s
canopy, i.e., average cover above the nest was 81.8% whereas below
it was 24.3%. Tree height was therefore omitted from analyses.
RESULTS
We monitored 106 fantail nesting attempts, 67 of these were
discovered at the nest-building phase, 16 at incubation stage and
23 with nestlings already present in the nest. Nests were located in
22 different tree species, especially kawakawa (Piper excelsum, 22
nests), mahoe (Melicytus ramiflorus, 16 nests), and karaka trees
(Corynocarpus laevigatus, 15 nests). A total of 68 breeding pairs
fledged on average of 1.3 chicks per nesting attempt; the average
number of clutches per breeding pair per season was 1.6, with a
maximum of four clutches in a season identified for one pair. Sixty-
seven nests were monitored with cameras.
Over the complete breeding season (2015–2016), fantail nesting
success in unfenced urban reserves was 44.5% (CI 95% = 30.0–
58.2). For both seasons, rats were responsible for most fantail nest
predations outside Zealandia (14 / 26 predations) with ship rats
the only invasive mammal observed on camera. The structure and
lining of the concluded nest appeared undisturbed for all
predation-unknown nests, and 6 of the 14 predation-rat nests, i.e.,
“Clean” (sensu Brown et al. 1998). Although no direct observation
of predation upon adult fantails was made, an adult went missing
from the breeding pair after 12 of 23 nest predation events. Three
avian predation events were recorded: two by the introduced
Blackbird (Turdus merula) and one by the native owl (Ninox
novaeseelandiae). Outcomes are shown for all nests in Table 1 and
in Fig. A1.2 in Appendix 1. Estimates of rat abundance (from chew-
cards and tracking tunnels) and nesting outcomes per site are
available in Table A1.1 in Appendix 1.
Table 1. Classification of nesting outcomes for New Zealand
Fantail (Rhipidura fuliginosa placabilis, North Island subspecies)
nests monitored from 2014–2016 across Wellington City forested
reserves. Zealandia is a fenced reserve from which rats, mustelids,
and possums have been completely removed.
2014–2015 2015–2016
Number Wellington Wellington Zealandia
Nests Commenced 25 65 16
Abandoned 1 13 2
Deserted 0 4 0
Spilled 1 1 1
Predation-Rat 2 12 0
Predation-Bird 1 0 2
Predation-Unknown 3 8 1
Successful 17 27 10
Daily survival rates 0.983 0.974 0.987
Nesting success† (CI 95%) 58.0 (30–78) 44.5 (30–58) 66.4 (34–86)
†Nesting success = daily survival rate ^ 31 (31 days is the average nesting
period for the NZ Fantail). This estimate can be calculated only for nests
that received a clutch.
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
Table 2. Multimodel assessment of the influence of nest placement, season, and weather variables on nest abandonment (n = 67, where
∆AICc < 4, i.e., 28 of 128 models presented). All models include a constant intercept term and a random term for New Zealand Fantail
(Rhipidura fuliginosa placabilis, North Island subspecies) breeding pair. Summed model weights and averaged beta estimates (plus
error) are calculated from the full model set.
nest
height
(m)
season
day
checks/
day
min.
temp
(°C)
max.
wind
(m/s)
total
rain
(mm)
year branch
width
(mm)
K†logLik‡∆§
AICc
Wi
|
X X 4 -31.18 0.00 0.12
X 3 -32.43 0.23 0.10
X X X 5 -30.71 1.39 0.06
X X X 5 -30.83 1.63 0.05
X X X 5 -30.95 1.88 0.05
X X 4 -32.19 2.01 0.04
X X 4 -32.19 2.02 0.04
X X X 5 -31.03 2.03 0.04
X X 4 -32.22 2.08 0.04
X X 4 -32.23 2.09 0.04
X X X 5 -31.08 2.14 0.04
X X X 5 -31.17 2.32 0.04
X X 4 -32.40 2.44 0.03
X X 4 -32.43 2.49 0.03
X X X X 6 -30.39 3.17 0.02
X X X X 6 -30.49 3.37 0.02
X X X 5 -31.72 3.41 0.02
X X X X 6 -30.54 3.47 0.02
X X X X 6 -30.57 3.53 0.02
X X X 5 -31.79 3.55 0.02
X X X X 6 -30.59 3.57 0.02
X X X 5 -31.83 3.63 0.02
X X X X 6 -30.65 3.69 0.02
X X X X 6 -30.68 3.76 0.02
X X X X 6 -30.69 3.78 0.02
X X X X 6 -30.70 3.79 0.02
X X X 5 -32.00 3.97 0.02
X X X X 6 -30.80 3.99 0.02
0.94 0.55 0.31 0.29 0.28 0.27 0.25 0.24 = summed model weights
0.35 -0.02 1.51 0.05 0.04 0.01 0.36 -0.05 = averaged β estimate¶
0.14 0.01 1.72 0.26 0.07 0.02 1.42 0.18 = averaged β standard error
1.42#0.99 4.53 1.05 1.04 1.01 1.43 0.95 = odds ratio (exp(β))
†number of parameters;
‡the maximized log-likelihood function;
§difference in AICc value for model relative to the top model;
|the AICc weight for each model in the set of candidate models;
¶the effect of a unit increase in the parameter value, upon relative probability of abandonment;
#95% CI of the odds ratio does not include 1.
The model that assumed constant survival across the season fitted
the data best for all time-dependent models (Table A1.2 in
Appendix 1). The DSR was lower for nests with chicks than those
with eggs, however a likelihood ratio test showed that this was not
statistically significant (χ2 = 0.426, df1, P = 0.514). We therefore
fitted a constant intercept term for all survival analyzed in
program Mark.
Factors affecting nest abandonment
Nest height (mean 3.25, SE 0.30, range 1.2–15 m) was the most
influential parameter for predicting nest abandonment (Z65 =
2.38, P = 0.017; Table 2) with abandonment of 4 out of 5 nests
located above 7 meters. Date was also present in the top model as
nest abandonment occurred less often as the breeding season
advanced and no nests were abandoned after 1 January (Fig. A1.2,
Appendix 1). Summed weights for each variable (from full model
averaging) were nest height 0.94, date 0.57, nest checks per day
0.32, minimum temperature 0.30, total rain 0.29, branch width
0.28, and maximum wind gust 0.25.
Rat density impact function
The likelihood of nest failure apparently increased as the chew-
card index for rat abundance increased (mean = 6.36, SE = 1.79,
range 0–66.7) although this trend was not significant (Fig. 1; as
calculated from the full model set: Z54 = 1.71, P = 0.087). Once
the chew-card index (CCI) reached 25%, the probability of a nest
failing due to predation exceeded 50%. At 45% CCI the expected
probability of nest predation approached 80%, however only two
nests were monitored at sites with rat abundance above 40% CCI.
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
Table 3. Multimodel assessment of the influence of nest placement and rat abundance on the daily nest survival of a New Zealand
Fantail (Rhipidura fuliginosa placabilis, North Island subspecies) nest as calculated in program Mark (n = 61, where ∆AICc < 4, i.e.,
10 of 32 models presented). All models include a constant intercept term. Summed model weights and averaged beta estimates (plus
error) are calculated from the full model set.
branch width
(mm)
rat abundance
(%)
nest height
(m)
cover above
(%)
cover below
(%)
K†logLik‡∆§
AICc
Wi
|
X X 3 1.00 0.00 0.18
X X X 4 0.75 0.58 0.14
X X X 4 0.58 1.10 0.10
X X X 4 0.37 2.01 0.07
X 2 0.35 2.07 0.06
X X X X 5 0.34 2.15 0.06
X X 3 0.28 2.53 0.05
X X X X 5 0.28 2.55 0.05
X X X X 5 0.21 3.12 0.04
X X 3 0.15 3.77 0.03
0.89 0.72 0.42 0.33 0.28 = summed model weights
-0.33 -0.03 -0.14 -0.01 0.00 = averaged β estimate¶
0.12 0.01 0.11 0.01 0.03 = averaged β standard error
1.38#1.03#1.15 1.01 1.00 = odds ratio
†number of parameters;
‡the maximized log-likelihood function;
§difference in AICc value for parameter relative to the top parameter;
|the AICc weight for each model in the set of candidate models;
¶the effect of a unit increase in the parameter value, upon relative probability of predation;
#95% CI of the odds ratio does not include 1.
Fig. 1. Density impact function relating the observed
abundance of ship rats (Rattus rattus) to the expected failure
rate of New Zealand Fantail (Rhipidura fuliginosa placabilis,
North Island subspecies) nests due to mammalian predation.
The curved line represents the predicted probability of nest
failure (right-hand y-axis) for a given index of rat abundance.
This analysis excludes nests that were abandoned, or failed due
to desertion, bad weather, or bird predation (n = 57). This
analysis also excludes a single nest that failed to an unknown
predator in Zealandia (where all mammals, except mice, are
excluded). Fates of individual nests (circles) incorporate a
random component (i.e., the binary outcome was “jittered”)
along the axis of rat abundance to reduce overlap and to
illustrate sample sizes more clearly.
The effects of nest-site on nest survival from
predation
Branch width and rat abundance were the most highly weighted
variables in the multimodel analysis of predation followed by nest
height, cover above, and cover below (Table 3). Nests on thin
branches had significantly higher daily survival rates than nests
on thicker branches across a wide range of rat abundance (Fig.
2). The average width of nest branches in this study was 8mm
(IQR = 8–10 mm).
Fig. 2. Predicted daily survival rates of New Zealand Fantail
(Rhipidura fuliginosa placabilis, North Island subspecies) nests,
modeled according to varying rat abundance (x-axis) for nests
located on branches of two different widths: dashed line = 6
mm diameter (the minimum observed) and dotted line = 15 mm
(the maximum observed). Relationships shown are mean ± 95%
confidence interval (line ± shaded band).
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
The presence of cameras did not significantly alter predation of
nests with the proportion of successful nests with cameras (33 /
56) no greater than for nests without cameras (11 / 18; binomial
test: P = 0.68). In Figure 3, we provide an illustration of the
proportions of nesting outcomes for the 2015–2016 breeding
season, plus key findings from this study.
Fig. 3. Diagram showing proportional fates for 81 New
Zealand Fantail (Rhipidura fuliginosa placabilis, North Island
subspecies) nesting attempts across forested reserves in
Wellington City, 2015–2016. Bullet points state the key findings
from the study. Illustration by David Young.
DISCUSSION
Factors affecting nest abandonment
Nests built higher in the canopy and in the earlier weeks of the
breeding season had a greater probability of being abandoned,
which suggests abandonment may be triggered by exposure to
inclement weather. Higher rates of nest abandonment in the
earlier weeks of the breeding season were also reported by
Maddox and Weatherhead (2006) for the Common Grackle
(Quiscalus quiscula). We were unable to link nest abandonment
to particular weather events, however we sourced our climate data
from a central Wellington site and these data do not detail the
specific nest-site conditions, such as the extent of nest exposure
to the cold, wind, or rain. Additionally, nest abandonment dates
are estimates with accuracy determined by the interval between
checks. Nevertheless, our result is consistent with anecdotal
evidence from other studies on the effects of weather in limiting
nest survival of New Zealand Fantails (Blackburn 1966,
Powlesland 1982, Miskelly and Sagar 2008).
Our study also shows that nest survival during the earliest stage
of nesting, prior to egg-laying, is tenuous. The model including
checks per day (the number of observer checks before nest laying/
abandonment expressed as a daily rate) ranked within the top
three best models and showed an increased probability of
abandonment with more frequent nest checks. Therefore, it is
justified for researchers to exercise caution and minimize human
presence at nests yet to receive clutches.
Although not investigated in this study, the presence of predators
near the nest is also likely to trigger nest-abandonment. Berger-
Tal et al. (2010) tested causes of nest abandonment in Australian
Fantails using mounted models of large birds at Grey Fantail
nests (Rhipidura albiscapa), a species until recently described as
conspecific with the New Zealand Fantail (Schodde et al. 1999,
Christidis and Boles 2008). They found nests were abandoned
only when models of a known predatory bird were presented.
High rates of nest abandonment (47%) were reported by Munro
(2007) for Grey Fantails in a study recording exceptionally high
nest predation (83% annual average). Furthermore, some
abandoned nests are likely to be the result of “cryptic predation”
with nests being laid in and subsequently preyed upon between
nest-checks, predation therefore going undetected (Maddox and
Weatherhead 2006). Other invasive mammalian predators such
as mustelids (Mustela spp.) and mice (Mus musculus) are likely to
also prey upon fantail nests (Moors 1983) yet their speed or small
size may lessen the likelihood of detection by cameras.
Because fantail pairs that abandoned nests did not abandon a
second time in the same season (see Fig. A1.2), it is possible that
breeding pairs adjust nest placement in a reactive manner where
threats are detected. New Zealand Fantails are short lived (average
life span of 1 year; Heather et al. 2015) and adults therefore have
little chance to refine nesting behavior across multiple breeding
seasons, as has been shown for another small passerine (Horie
and Takagi 2012). Yet, they have high renesting potential and, as
shown for the closely related Grey Fantail (Beckmann et al. 2015,
Flegeltaub et al. 2017) and the Australian Bell Miner (Manorina
melanophrys; Beckmann and McDonald 2016), adaptive
renesting behavior can improve nesting success.
Nest predation
There was an apparent (near significant) relationship between the
chew-card index of localized ship rat abundance and fantail
nesting success, because 4 out of 5 nests failed where rat
abundance was moderate (30–40 % CCI), however, we were unable
to model the full range of rat abundance indices in our study. We
spent considerable effort trying to locate nests in sites where rat
abundance was high, however only two nests were found at sites
above 40% CCI. The lack of nests within such sites might reflect
a lower density of breeding pairs of fantails in reserves where rat
abundance is high. Results from a study in the North Island report
the highest rates of nesting success for nesting fantails (36%) when
rat abundance is low-moderate (< 30% of tunnels tracked) and <
10% nesting success where rat abundance was high (> 70%; Sutton
et al. 2012). In our study, the density impact function of rats upon
nesting fantails followed a proportionate relationship (Norbury
et al. 2015) and where rat abundance was highest (e.g., ≥ 30%
CCI), predation on the nest was high, i.e., predation on 4 out of
5 nests. Consequently, an extrapolation of our data suggests
survival rates will be low where rat densities are high, i.e., 80%
likelihood of nest predation above 50% CCI) and results from
this research and Sutton et al. (2012) show very few fantails raise
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
young to the fledgling stage under conditions of high rat
abundance.
Although there was no clear relationship between the tracking
tunnel index of rat abundance and fantail nesting success, this
mostly reflects a difference in scale between our two rodent
abundance indices. The nine chew-cards placed around a nest gave
a highly localized estimate of ship rat abundance that may be due
to just one or a few individual rats in the immediate vicinity of
the nest, whereas the tracking tunnel transects provides a more
generalized estimate of relative rat abundance at the scale of entire
reserves. Tracking tunnel indices have been shown to correspond
to actual densities (Brown et al. 1996, Innes et al. 2010b, Christie
et al. 2015) but may be less reliable for estimating rats in lower
densities (Blackwell et al. 2002) and results may vary with seasons
(Christie et al. 2015).
The width of the nest branch and the chew-card index of rodent
abundance were the factors that most influenced the probability
of nest survival against predation and, according to our results,
nests built on thinner branches were afforded some level of
protection from predation by rats. Branch width has not
previously been shown to limit rat predation on arboreal nests,
however nests of Warbling Vireo (Vireo gilvus) located on thinner
branches fared better in areas where squirrels (Tamiasciurus
douglasii) were the main nest predator (Smith et al. 2005). Whereas
the small fantail appears to locate nests on the thinner, outer
branches (personal observation), studies of larger species have
shown selection for nest placement on more stout branches that
are closer to the main stem. This is thought to provide greater
support for their nest structures but also for increased
concealment as foliage is often more dense closer to the main stem
(Zhou et al. 2011).
An evolutionary history of mammalian predation pressure is
likely to have shaped nest-site choice in the New Zealand Fantails
because these species are closely related to the Australian Grey
Fantail (Schodde et al. 1999, Nyári et al. 2009) whose range
throughout the southwest pacific supports native, mammalian
predators. Additionally, in Zealandia, where mammalian nest
predators are removed from the system, avian predation of fantail
nests featured more prominently (Table 1), which suggests that
mammalian predation on New Zealand Fantail nests may to some
extent be compensatory (Newton 1998) with invasive mammals
preying upon nests that might otherwise fail to avian predation
and vice versa. However, in the New Zealand context, increased
avian predation of fantail nests may not necessarily be a sign of
enhanced ecological integrity because two of the three avian
predations were by an introduced species (Blackbird). Fantail
nests were typically located directly under the upper canopy
(resulting in a strong correlation between nest height and tree
height). This is consistent with avian predation influencing the
selection of nest-site because research has shown that increased
concealment of nests from above is particularly effective in
thwarting avian predators (Brown 1997, Remeš 2005). Nest
placement therefore involves trade-offs, such that a nest placed
on the thinner-branched, outer reaches of a tree might limit
approaches from climbing mammals, yet increases its exposure,
making it more vulnerable to avian predators or weather.
Our study shows nest survival of the New Zealand Fantail to be
strongly affected by rats. Most reserves in Wellington City are
under some form of management to maintain low densities of
rats, yet even across this range, fantails suffered significantly
heavier losses on nests located at sites where rat abundance was
higher. Indeed, the average nesting success of fantails across
unfenced reserves in Wellington City was only 44.5% (2015–2016;
Table 1), where average rat tracking was low (6.3% in 2015–2016;
Table A1.1). However, this nesting success rate is comparatively
high when compared to success rates of the Grey Fantail in
Australia (17% nesting success; Munro 2007), where native
predators cause considerable losses (Flegeltaub et al. 2017).
Fantail nesting success in our study was similar to that recorded
for North Island Robins where rat abundance was also low (i.e.,
≤ 50% nesting success where tracking rate < 5% tracking rate;
Armstrong et al. 2006) and comparable to nesting success rates
of a small, endangered monarch flycatcher in Hawaii, the O'ahu
'Elepaio (Chasiempis sandwichensis ibidis) when numbers of ship
rats were markedly reduced (Vanderwerf and Smith 2002). Ship
rats continue to exert considerable pressure even when abundance
is low, and this has important implications for conservation of
less resilient endemic birds.
The Grey Fantail in Australia withstands high rates of nest
predation (Munro 2007), and in New Zealand, Blackburn (1966)
observed two pairs of fantails fledging a total of 16 young in one
breeding season, despite frequent predation and low nesting
success (6 / 13 nests successful). The New Zealand Fantail appears
to be capable of compensating for moderate levels of nest failure
because the birds mature early, i.e., they are able to breed in the
first year (Powlesland 1982) and have a high reproductive rate:
two life history parameters shown to be important in determining
population growth rate potential (Stahl and Oli 2006). However
high rates of nest predation by ship rats are likely to limit
populations of fantails and other small New Zealand birds that
exhibit similar strategies. Populations of small, endemic birds in
New Zealand forests, where high densities of invasive rats are the
norm (Efford et al. 2006, Ruscoe et al. 2013), are therefore likely
to be severely limited and effective conservation of this group,
that evolved in the absence of mammalian predators, is likely to
require ongoing management of rat populations to low levels.
Responses to this article can be read online at:
http://www.ace-eco.org/issues/responses.php/1284
Acknowledgments:
We thank all the members of the public who helped us locate nests,
and to the team at Zealandia for access and support. We are also
grateful to John Innes and an anonymous reviewer for comments on
earlier drafts. This doctoral research was primarily supported by the
Holdsworth Charitable Trust [grant number 80759-2268]. It was
also assisted by student awards from the Victoria University of
Wellington Centre for Biodiversity and Restoration Ecology
[80189-2268], Victoria University of Wellington Faculty for
Strategic Research [212746-3619], and a Pukaha Mount Bruce
Elwin Welch Memorial grant [2014].
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
LITERATURE CITED
Amiot, C., W. Ji, and S. D. Hill. 2015. Using plumage and
behavioural development to age New Zealand Fantail nestlings.
New Zealand Journal of Zoology 42:35-43. http://dx.doi.
org/10.1080/03014223.2014.980749
Armstrong, D. P., E. H. Raeburn, R. M. Lewis, and D. Ravine.
2006. Modeling vital rates of a reintroduced New Zealand robin
population as a function of predator control. Journal of Wildlife
Management 70:1028-1036. http://dx.doi.org/10.2193/0022-541X
(2006)70[1028:MVROAR]2.0.CO;2
Atkinson, I. A. E. 1985. The spread of commensal species of
Rattus to oceanic islands and their effects on island avifaunas.
Pages 35-81 in P. J. Moors, editor. Conservation of island birds.
Technical Publication No. 3 edition, International Council for
Bird Preservation, Cambridge, UK.
Barton, K. 2016. MuMIn: multi-model inference R package
(Version 1.15.6). Vienna, Austria. [online] URL: http://CRAN.
R-project.org/package=MuMIn/
Bates, D., M. Mächler, B. M. Bolker, and S. C. Walker. 2015.
Fitting linear mixed-effects models using lme4. Journal of
Statistical Software 67:1-48. http://dx.doi.org/10.18637/jss.v067.
i01
Beckmann, C., P. A. Biro, and K. Martin. 2015. Hierarchical
analysis of avian re-nesting behavior: mean, across-individual,
and intra-individual responses. Behavioral Ecology and
Sociobiology 69:1631-1638. http://dx.doi.org/10.1007/s00265-015-1974-1
Beckmann, C., and P. G. McDonald. 2016. Placement of re-nests
following predation: are birds managing risk? Emu 116:9-13.
http://dx.doi.org/10.1071/MU15064
Berger-Tal, R., O. Berger-Tal, and K. Munro. 2010. Nest desertion
by Grey Fantails during nest building in response to perceived
predation risk. Journal of Field Ornithology 81:151-154. http://dx.
doi.org/10.1111/j.1557-9263.2010.00272.x
Blackburn, A. 1966. Some further observations on the nesting of
the North Island Fantail. Notornis 13:189-196.
Blackburn, T. M., P. Cassey, R. P. Duncan, K. L. Evans, and K.
J. Gaston. 2004. Avian extinction and mammalian introductions
on Oceanic Islands. Science 305:1955-1958. http://dx.doi.
org/10.1126/science.1101617
Blackwell, G. L., M. A. Potter, and J. A. McLennan. 2002. Rodent
density indices from tracking tunnels, snap-traps and fenn traps:
do they tell the same story? New Zealand Journal of Ecology
26:43-51.
Brown, K. P. 1997. Predation at nests of two New Zealand
endemic passerines; implications for bird community restoration.
Pacific Conservation Biology 3:91-98. http://dx.doi.org/10.1071/
PC970091
Brown, K. P., H. Moller, J. Innes, and N. Alterio. 1996. Calibration
of tunnel tracking rates to estimate relative abundance of ship
rats (Rattus rattus) and mice (Mus musculus) in a New Zealand
forest. New Zealand Journal of Ecology 20:271-275.
Brown, K. P., H. Moller, J. Innes, and P. Jansen. 1998. Identifying
predators at nests of small birds in a New Zealand forest. Ibis
140:274-279. http://dx.doi.org/10.1111/j.1474-919X.1998.tb04389.
x
Burnham, K. P., and D. R. Anderson. 2002. Model selection and
multimodel inference: a practical information-theoretic approach.
Springer Science & Business Media, New York, New York, USA.
Christidis, L., and W. E. Boles. 2008. Systematics and taxonomy
of Australian birds. CSIRO, Collingwood, Australia.
Christie, J. E., D. I. MacKenzie, T. C. Greene, and J. L. Sim. 2015.
Using passive detection devices to monitor occupancy of ship rats
(Rattus rattus) in New Zealand temperate rainforest. New Zealand
Journal of Ecology 39:79-86.
Cohen, J. E., S. L. Pimm, P. Yodzis, and J. Saldana. 1993. Body
sizes of animal predators and animal prey in food webs. Journal
of Animal Ecology 62:67-78. http://dx.doi.org/10.2307/5483
Colombelli-Négrel, D., and S. Kleindorfer. 2009. Nest height, nest
concealment, and predator type predict nest predation in superb
fairy-wrens (Malurus cyaneus). Ecological Research 24:921-928.
http://dx.doi.org/10.1007/s11284-008-0569-y
Conrey, R. Y., S. K. Skagen, A. A. Y. Adams, and A. O. Panjabi.
2016. Extremes of heat, drought and precipitation depress
reproductive performance in shortgrass prairie passerines. Ibis
158:614-629. http://dx.doi.org/10.1111/ibi.12373
Courchamp, F., J.-L. Chapuis, and M. Pascal. 2003. Mammal
invaders on islands: impact, control and control impact.
Biological Reviews 78:347-383. http://dx.doi.org/10.1017/
S1464793102006061
Department of Conservation. 2014. The Foliar browse index field
manual. An update of a method for monitoring possum
(Trichosurus vulpecula) damage to forest communities.
Department of Conservation, Christchurch, New Zealand.
[online] URL: https://nvs.landcareresearch.co.nz/Content/
FOLIAR_BROWSE_INDEX.pdf
Dinsmore, S. J., and J. J. Dinsmore. 2007. Modeling avian nest
survival in program mark. Studies in Avian Biology 34:73-83.
Doherty, T. S., A. S. Glen, D. G. Nimmo, E. G. Ritchie, and C.
R. Dickman. 2016. Invasive predators and global biodiversity
loss. Proceedings of the National Academy of Sciences of the
United States of America 113:11261-11265. http://dx.doi.
org/10.1073/pnas.1602480113
Duron, Q., A. B. Shiels, and E. Vidal. 2017. Control of invasive
rats on islands and priorities for future action. Conservation
Biology 31:761-771. http://dx.doi.org/10.1111/cobi.12885
Efford, M. G., B. M. Fitzgerald, B. J. Karl, and P. H. Berben.
2006. Population dynamics of the ship rat Rattus rattus L. in the
Orongorongo Valley, New Zealand. New Zealand Journal of
Zoology 33:273-297. http://dx.doi.org/10.1080/03014223.2006.9518457
Elliott, G. P., P. J. Dilks, and C. F. J. O'Donnell. 1996. Nest site
selection by Mohua and Yellow-crowned Parakeets in beech forest
in Fiordland, New Zealand. New Zealand Journal of Zoology
23:267-278. http://dx.doi.org/10.1080/03014223.1996.9518085
Elliott, G. P., J. Kemp, and J. C. Russell. 2018. Estimating
population growth rates from tracking tunnels. New Zealand
Journal of Ecology 42(2):269-272. http://dx.doi.org/10.20417/
nzjecol.42.19
Empson, R., and D. Fastier. 2013. Translocations of North Island
Tomtits (Petroica macrocephala toitoi) and North Island Robins
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
(P. longipes) to Zealandia-Karori Sanctuary, an urban sanctuary.
What have we learned? Notornis 60:63-69.
Flegeltaub, M., P. A. Biro, and C. Beckmann. 2017. Avian nest
abandonment prior to laying-a strategy to minimize predation
risk? Journal of Ornithology 158:1091-1098. http://dx.doi.
org/10.1007/s10336-017-1470-7
Foster, S., C. King, B. Patty, and S. Miller. 2011. Tree-climbing
capabilities of Norway and ship rats. New Zealand Journal of
Zoology 38:285-296. http://dx.doi.org/10.1080/03014223.2011.599400
Gabites, I. 1993. Wellington’s living cloak. Victoria University
Press, Wellington, New Zealand.
Gillies, C. A., M. R. Leach, N. B. Coad, S. W. Theobald, J.
Campbell, T. Herbert, P. J. Graham, and R. J. Pierce. 2003. Six
years of intensive pest mammal control at Trounson Kauri Park,
a Department of Conservation “mainland island”, June 1996-
July 2002. New Zealand Journal of Zoology 30:399-420. http://dx.
doi.org/10.1080/03014223.2003.9518349
Gillies, C., and D. Williams. 2013. DOC tracking tunnel guide
v2.5.2: using tracking tunnels to monitor rodents and mustelids.
Department of Conversation, Science & Capability Group,
Hamilton, New Zealand. [online] URL: http://www.doc.govt.nz/
Documents/science-and-technical/inventory-monitoring/im-toolbox-
animal-pests-using-tracking-tunnels-to-monitor-rodents-and-mustelids.
pdf
Griffiths, J. W., and M. C. Barron. 2016. Spatiotemporal changes
in relative rat (Rattus rattus) abundance following large-scale pest
control. New Zealand Journal of Ecology 40:371-380. http://dx.
doi.org/10.20417/nzjecol.40.33
Heather, B. D., H. A. Robertson, and D. Onley. 2015. The field
guide to the birds of New Zealand. Viking-Penguin Books,
Auckland, New Zealand.
Holdaway, R. N., T. H. Worthy, and A. J. D. Tennyson. 2001. A
working list of breeding bird species of the New Zealand region
at first human contact. New Zealand Journal of Zoology
28:119-187. http://dx.doi.org/10.1080/03014223.2001.9518262
Horie, S., and M. Takagi. 2012. Nest positioning by male Daito
White-eyes Zosterops japonicus daitoensis improves with age to
reduce nest predation risk. Ibis 154:285-295. http://dx.doi.
org/10.1111/j.1474-919X.2011.01204.x
Innes, J., R. Hay, I. Flux, P. Bradfield, H. Speed, and P. Jansen.
1999. Successful recovery of North Island Kokako Callaeas
cinerea wilsoni populations, by adaptive management. Biological
Conservation 87:201-214. http://dx.doi.org/10.1016/S0006-3207
(98)00053-6
Innes, J., D. Kelly, J. Overton, and C. Gillies. 2010a. Predation
and other factors currently limiting New Zealand forest birds.
New Zealand Journal of Ecology 34:86-114.
Innes, J., C. M. King, L. Bridgman, N. Fitzgerald, G. Arnold, and
N. Cox. 2010b. Effect of grazing on ship rat density in forest
fragments of lowland Waikato, New Zealand. New Zealand
Journal of Ecology 34:227-232.
Innes, J., B. Warburton, D. Williams, H. Speed, and P. Bradfield.
1995. Large-scale poisoning of ship rats (Rattus rattus) in
indigenous forests of the North-Island, New-Zealand. New
Zealand Journal of Ecology 19:5-17.
Johnson, T. H., and A. J. Stattersfield. 1990. A global review of
island endemic birds. Ibis 132:167-180. http://dx.doi.org/10.1111/
j.1474-919X.1990.tb01036.x
King, C. M. 2005. The handbook of New Zealand mammals.
Second edition. Oxford University Press, Melbourne, Australia.
Maddox, J. D., and P. J. Weatherhead. 2006. Nests without eggs:
abandonment or cryptic predation? Auk 123:135-140. http://dx.
doi.org/10.1642/0004-8038(2006)123[0135:NWEAOC]2.0.CO;2
Martin, T. E. 1993. Nest predation and nest sites - new
perspectives on old patterns. Bioscience 43:523-532. http://dx.doi.
org/10.2307/1311947
Massaro, M., A. Starling-Windhof, J. V. Briskie, and T. E. Martin.
2008. Introduced mammalian predators induce behavioural
changes in parental care in an endemic New Zealand bird. PLoS
ONE 3:e2331. http://dx.doi.org/10.1371/journal.pone.0002331
Mayfield, H. F. 1975. Suggestions for calculating nest success.
Wilson Bulletin 87:456-466.
Miskelly, C. M., and P. M. Sagar. 2008. Establishment and local
extinction of fantails (Rhipidura fuliginosa) on the Snares Islands,
New Zealand. Notornis 55:170-171.
Moors, P. J. 1983. Predation by mustelids and rodents on the eggs
and chicks of native and introduced birds in Kowhai Bush, New-
Zealand. Ibis 125:137-154. http://dx.doi.org/10.1111/j.1474-919X.1983.
tb03095.x
Mudge, D. 2002. Silence of the fantails. New Zealand Geographic
55:71-85.
Munro, K. 2007. Breeding behaviour and ecology of the Grey
Fantail (Rhipidura albiscapa). Australian Journal of Zoology
55:257-265. http://dx.doi.org/10.1071/ZO07025
Newton, I. 1998. Population limitation in birds. Academic, San
Diego, California, USA.
Norbury, G. L., R. P. Pech, A. E. Byrom, and J. Innes. 2015.
Density-impact functions for terrestrial vertebrate pests and
indigenous biota: guidelines for conservation managers.
Biological Conservation 191:409-420. http://dx.doi.org/10.1016/j.
biocon.2015.07.031
Nyári, A. S., B. W. Benz, K. A. Jønsson, J. Fjeldså, and R. G.
Moyle. 2009. Phylogenetic relationships of fantails (Aves:
Rhipiduridae). Zoologica Scripta 38:553-561. http://dx.doi.
org/10.1111/j.1463-6409.2009.00397.x
Payton, M. E., M. H. Greenstone, and N. Schenker. 2003.
Overlapping confidence intervals or standard error intervals:
What do they mean in terms of statistical significance? Journal of
Insect Science 3:34. http://dx.doi.org/10.1093/jis/3.1.34
Powlesland, M. H. 1982. A breeding study of the South Island
Fantail Rhipidura fuliginosa fuliginosa. Notornis 29:181-195.
Remeš, V. 2005. Birds and rodents destroy different nests: a study
of Blackcap Sylvia atricapilla using the removal of nest
concealment. Ibis 147:213-216. http://dx.doi.org/10.1111/
j.1474-919X.2004.00339.x
Avian Conservation and Ecology 13(2): 11
http://www.ace-eco.org/vol13/iss2/art11/
Richardson, T. W., T. Gardali, and S. H. Jenkins. 2009. Review
and meta-analysis of camera effects on avian nest success. Journal
of Wildlife Management 73:287-293. http://dx.doi.org/10.2193/2007-566
Robertson, H. A., J. R. Hay, E. K. Saul, and G. V. McCormack.
1994. Recovery of the Kakerori: an endangered forest bird of the
Cook Islands. Conservation Biology 8:1078-1086. http://dx.doi.
org/10.1046/j.1523-1739.1994.08041078.x
Rotella, J. 2015. Nest survival models. Chapter 17 in E. G. Cooch
and G. C. White, editors. Program MARK - a gentle introduction.
Program MARK, USA. [online] URL: http://www.phidot.org/
software/mark/docs/book/pdf/chap17.pdf
Ruscoe, W. A., D. S. L. Ramsey, R. P. Pech, P. J. Sweetapple, I.
Yockney, M. C. Barron, M. Perry, G. Nugent, R. Carran, R.
Warne, C. Brausch, and R. P. Duncan. 2011. Unexpected
consequences of control: competitive vs. predator release in a
four-species assemblage of invasive mammals. Ecology Letters
14:1035-1042. http://dx.doi.org/10.1111/j.1461-0248.2011.01673.
x
Ruscoe, W. A., P. J. Sweetapple, M. Perry, and R. P. Duncan. 2013.
Effects of spatially extensive control of invasive rats on abundance
of native invertebrates in mainland New Zealand forests.
Conservation Biology 27:74-82. http://dx.doi.org/10.1111/
j.1523-1739.2012.01932.x
Schodde, R., I. J. Mason, R. Schodde, and I. J. Mason. 1999. The
directory of Australian birds. A taxonomic and zoogeographic atlas
of the biodiversity of birds in Australia and its territories.
Passerines. CSIRO, Melbourne, Australia.
Shiels, A. B., W. C. Pitt, R. T. Sugihara, and G. W. Witmer. 2014.
Biology and impacts of Pacific Island invasive species. 11. Rattus
rattus, the Black Rat (Rodentia: Muridae). Pacific Science
68:145-184. http://dx.doi.org/10.2984/68.2.1
Smith, J. I., M. D. Reynolds, and G. LeBuhn. 2005. Warbling
Vireo reproductive success and nest-site characteristics in the
northern Sierra Nevada, California. Journal of Field Ornithology
76:383-389. http://dx.doi.org/10.1648/0273-8570-76.4.383
Stahl, J. T., and M. K. Oli. 2006. Relative importance of avian
life-history variables to population growth rate. Ecological
Modelling 198:23-39. http://dx.doi.org/10.1016/j.ecolmodel.2006.04.001
Stirnemann, R. L., M. A. Potter, D. Butler, and E. O. Minot. 2015.
Compounding effects of habitat fragmentation and predation on
bird nests. Austral Ecology 40:974-981. http://dx.doi.org/10.1111/
aec.12282
Sutton, N., J. Guillotel, and R. Potae. 2012. Tongariro Forest Kiwi
Sanctuary annual report, July 2011 - June 2012. Department of
Conversation, Wellington, New Zealand. [online] URL: http://
www.doc.govt.nz/Documents/conservation/native-animals/birds/
tfks-annual-report-2011-12.pdf
Sweetapple, P., and G. Nugent. 2011. Chew-track-cards: a
multiple-species small mammal detection device. New Zealand
Journal of Ecology 35:153-162.
Sweetapple, P. J., and G. Nugent. 2007. Ship rat demography and
diet following possum control in a mixed podocarp-hardwood
forest. New Zealand Journal of Ecology 31:186-201.
Towns, D. R., I. A. E. Atkinson, and C. H. Daugherty. 2006. Have
the harmful effects of introduced rats on islands been
exaggerated? Biological Invasions 8:863-891. http://dx.doi.
org/10.1007/s10530-005-0421-z
van Heezik, Y., K. Ludwig, S. Whitwell, and I. G. McLean. 2008.
Nest survival of birds in an urban environment in New Zealand.
New Zealand Journal of Ecology 32:155-165.
Vanderwerf, E. A. 2012. Evolution of nesting height in an
endangered Hawaiian forest bird in response to a non-native
predator. Conservation Biology 26:905-911. http://dx.doi.
org/10.1111/j.1523-1739.2012.01877.x
Vanderwerf, E. A., and D. G. Smith. 2002. Effects of alien rodent
control on demography of the O'ahu 'Elepaio, an endangered
Hawaiian forest bird. Pacific Conservation Biology 8:73-81. http://
dx.doi.org/10.1071/PC020073
White, G. C., and K. P. Burnham. 1999. Program MARK: survival
estimation from populations of marked animals. Bird Study 46:
S120-S139. http://dx.doi.org/10.1080/00063659909477239
Worthy, T. H., and R. N. Holdaway. 2002. The lost world of the
Moa: prehistoric life of New Zealand. Indiana University Press,
Bloomington, Indiana, USA.
Zhou, D., C. Zhou, X. Kong, and W. Deng. 2011. Nest-site
selection and nesting success of Grey-backed Thrushes in
northeast China. Wilson Journal of Ornithology 123:492-501.
http://dx.doi.org/10.1676/10-110.1
Editor-in-Chief: Keith A.Hobson
Subject Editor: Alexander L.Bond
Appendix 1. Additional information to support the online article is available in this appendix,
including: a table with rat abundance estimates and New Zealand Fantail (Rhipidura
fuliginosa placabilis, North Island subspecies) nest success for the Wellington sites (Table
A1.1); a table showing the multimodel assessment of the influence of time-dependent factors
on survival of fantail nests (Table A1.2); a figure showing the location of the study site in
New Zealand, plus the locations of nests in Wellington City (Fig. A1.1); and a figure where
individual fates for all nests found between 2014 and 2016 are plotted across the nesting
season (Fig. A1.2).
Table A1.1. Nesting outcomes by site for fantails in Wellington City.
2014-15
2015-16
Reserve
No.
Nests
No.
Success
% TT†
No.
Nests
No.
Success
% TT†
CCI‡
Birdwood
-
-
-
6
1
0
41
Central Park
-
-
-
11
6
0
0
Johnsonville
6
6
30
2
2
15
0
Ngaio
-
-
-
4
0
30
22
Otari-Wilton’s
5
4
2
2
1
5
0
Spicer’s Forest
-
-
-
7
4
0
0
Trelissick
10
5
15
13
9
0
7
Tyer’s Stream
4
2
30
3
0
0
7
Zealandia
-
-
-
16
10
0
0
Miscellaneous§
-
-
-
17
11
-
4
All Reserves
25
17
19.3
81
44
5.6
7.4
†percentage of tracking tunnel line (10 tunnels at 50m spacing per line) with rat tracking at
each site or the line average for sites with two lines (i.e Spicer’s Forest, Johnsonville and
Trelissick).
‡percentage of chew-cards (6-9 cards at 25m spacing per nest) with rat chew averaged
across each site.
§combination of all nesting outcomes and chew-card results only (i.e. no tracking tunnel
results available) from sites where ≤ 2 nest outcomes were gathered.
Table A1.2. Multimodel assessment of the influence of time-dependent factors on survival
of fantail nests as calculated in program Mark (n = 61). Factors include: nest phase
(chick/nestling), nest age, linear time (by season day) and season stage (early, middle or late
stage). All models include a constant intercept term.
constant
nest
phase
nest
age
linear
time
season
stage
K†
logLik‡
Ƥ
AICc
Wi|
X
1
1.00
0.00
0.36
X
X
2
0.45
1.58
0.17
X
X
2
0.38
1.95
0.14
X
X
2
0.37
1.95
0.13
X
X
2
0.24
2.88
0.09
X
X
X
3
0.18
3.50
0.06
X
X
X
3
0.14
3.96
0.05
†number of parameters.
‡the maximized log-likelihood function.
§difference in AICc value for parameter relative to the top parameter.
|the AICc weight for the model in the set.
Fig. A1.1. Location of study. Left map: New Zealand showing location of Wellington City; right map: fantail nest locations for 2014-15 (red
circles) and 2015-16 (black crosses). Colour key: green = forest (dark green = native / exotic mixed, light green = exotic forest); grey = buildings
and roading; brown = grassland / pasture; black outline = the Wellington City Council management boundary.
Fig. A1.2. Individual nest fates plotted across the season for the 68 fantail breeding pairs monitored in Wellington City reserves from 2014-
2016. Horizontal continuous lines spaced along a single row represent nest attempts of a pair of fantails from a single breeding season. Dashed
horizontal lines delineate breeding seasons (2014-15 and 2015-16) as well as nests from the second breeding season located in Zealandia, a
fenced eco-sanctuary where invasive mammals, except mice, have been removed. ‘Stage found’ describes the stage of the nest when first
discovered advancing from Building (parents were seen constructing the nest) to Incubating (nest had eggs) to Nestlings (nest had hatched
chicks). ‘Clutch first observed’ describes the day the nest was initially observed with eggs (i.e. this observation was not possible for nests
discovered at ‘Nestling’ stage). ‘Fate’ describes the outcome of the nest.
