Introduction
Density estimates are used to infer species ecology and guide
biodiversity conservation and management. Estimating density, however,
relies on successfully detecting animals, which is difficult for cryptic
or rare species (Garrard et al. , 2015). Probability of detection
can vary due to differences in survey method and effort, behaviour,
habitat, season and weather. Only a fraction of a population may be
detected during a survey, leading to a high risk of type two errors
(i.e., when non-detection at an occupied site is mistaken for evidence
of absence) (Guillera-Arroita, 2017, Kellner & Swihart, 2014) . Methods
to account for imperfect detectability include those that do not require
uniquely identifiable individuals (e.g., distance sampling and occupancy
modelling) (Buckland, 2001, MacKenzie et al. , 2018) and those
that require some fraction of the population to be uniquely identifiable
(e.g., capture-recapture and spatially explicit capture-recapture)
(Efford, 2004, Otis et al. , 1978, Pollock et al. , 1990).
However, even when accounting for imperfect detection, cryptic and rare
species generally have lower detection rates than common or conspicuous
ones (Dettmers et al. , 1999). Survey designs for wildlife must
therefore simultaneously account for imperfect detection, whilst
striving to optimise the underlying detection rates. Incorporating such
considerations can improve the accuracy and precision of density
estimates, enabling better-informed conservation management decisions.
Spatially explicit capture-recapture (SECR) modelling using photographic
identification is a popular technique for estimating densities of
species with distinctive individual markings (Green, Chynoweth &
Şekercioğlu, 2020). This method has been used with spotted or striped
cats (Green et al. , 2020) and distinctive small Australian
marsupials, including the sugar glider Petaurus notatus(Gracanin, Minchinton & Mikac, 2022) and numbat Myrmecobius
fasciatus (Thorn et al. , 2022). Unlike traditional
capture-recapture, SECR considers spatial variation in detection
probability and records animal capture and recapture locations. This
method enables estimation of population density across detector grids,
and informs capture probability and home range estimates (Efford,
Borchers & Byrom, 2009). To obtain a robust estimate of population
density with SECR, repeated observations of numerous individuals at
multiple detectors are required.
Arboreal nocturnal animals pose unusual challenges for survey design.
They are cryptic, occupy challenging terrain and forest strata, and have
variable detectability with traditional survey techniques (Catling, Burt
& Kooyman, 1997, Davey, 1990). Forest dependent fauna are often of high
conservation importance as forests globally are reduced and disturbed
(Lindenmayer, 2023, Potapov et al. , 2022), resulting in the
removal of mature habitat features and connectivity that such species
need to persist. Consequently, there is a pressing need for reliable
techniques that can be used to estimate population density for species
which are of high ecological significance or threatened. Despite the
biological and conservation importance of density information, few
species receive targeted innovation in developing detection methods. For
instance, most surveys of threatened arboreal marsupials that rely on
live capture techniques use highly generalized lures (Commonwealth of
Australia, 2011). In instances of detailed research (Austin et
al. , 2017, Diete et al. , 2016, Morgan, 1990, Wayne et
al. , 2005), substantial improvements can be made to tailor
attractiveness of baits to target species, which can dramatically
improve the probability of detection. Developing customized detection
solutions for a target species can therefore be the difference between
meaningful ecological inference and a failed study.
We consider a case study of an unusual ecological problem and aim to
develop an effective approach for estimating density of an arboreal
nocturnal marsupial. The sugar glider, Petaurus notatus (synonymP. breviceps, inland sugar glider, Krefft’s glider), is
widespread in eastern Australia (Cremona et al. , 2021, Goldingayet al. , 2023), and was introduced to Tasmania in the 1830’s
(Campbell et al. , 2018). Recent research has shown that sugar
gliders are unexpected major predators of nesting birds in Tasmania,
including the critically endangered swift parrot Lathamus
discolor (Stojanovic et al. , 2018, Stojanovic et al. ,
2014). There is an urgent need to understand the scale and severity of
the predation threat to swift parrots but basic information about
Tasmanian sugar gliders remains unknown.
Here, we aim to overcome this conservation problem regarding sugar
gliders in three steps. First, we review the literature and evaluate the
range and effectiveness of methods available for trapping sugar gliders.
Next, we undertake a field study to evaluate approaches for optimizing
detection. Detection probability can be influenced by survey method,
behaviour, habitat, season and weather. Low detection probability can
result in wrongly concluding the target species is absent and cause high
uncertainty in resulting estimates of density. Cryptic species like the
sugar glider generally have lower detection rates than common or
conspicuous ones and survey designs must therefore aim to increase
underlying detection rates while accounting for imperfect detection.
Incorporating such considerations can improve the accuracy and precision
of density estimates, enabling better-informed conservation management
decisions. We do this by undertaking a camera trapping study and use
SECR analysis to determine which types of bait influence detectability.
Finally, we evaluate density of sugar gliders using SECR at a swift
parrot important breeding area. Density estimates are crucial to infer
species ecology and guide biodiversity conservation and management. We
discuss our results in the context of conservation needs of swift
parrots and research approaches for sugar gliders.