Introduction
A core aim of conservation management is optimising habitat quality for
focal species (Johnson, 2005, McComb, 2016). For management to be truly
optimised, a measurable understanding of what constitutes habitat
quality is required (Marzluff et al., 2000). The ultimate measure of
habitat quality for an individual is the individual’s relative
contribution to the growth rate of the population when inhabiting a
given habitat (Johnson, 2007). There are two components of this measure:
survival and reproduction. By defining habitat quality in terms of
population growth rate, habitat quality can be assessed on a continuous
temporal scale. For example, habitat quality can be measured
instantaneously or as a life-time measure of habitat quality akin to the
individual’s fitness. There are many components that combine to
influence survival and reproductive output including food availability,
predation risk, habitat structure and configuration, and the presence of
disturbances (e.g., human foot traffic) (Johnson, 2007).
Quantifying demographic rates (survival and reproductive output) is a
challenging task (Stephens et al., 2015), as it requires sustained
monitoring of individuals of known-identity. Studies that do achieve
this are often conducted either on sessile organisms (e.g., Ma et al.,
2014, Wang et al., 2012, Zhao et al., 2006) or large-bodied organisms
that are restricted to a small geographic area (e.g., islands: (Kruuk et
al., 1999, Richard et al., 2014); natal colony: (Baker and Thompson,
2007, Le Boeuf et al., 2019)). Demographic rates are also financially
costly to measure (Knutson et al., 2006, Pidgeon et al., 2006), and the
long time-frames for data collection can mean that research extends
beyond typical funding cycles and research project lifetimes,
particularly for research on long-lived species (Le Boeuf et al., 2019).
Despite these challenges, there have been studies that successfully
monitor survival (Valdez-Juarez et al., 2019) and reproductive
performance (Pérot and Villard, 2009, Pidgeon et al., 2006, Zanette,
2001) of birds in relation to habitat quality. Outputs from these
studies are often very applied with actionable recommendations for
conservation decision-makers.
Waterbirds are a particularly challenging group to obtain habitat
quality estimates for because multiple factors can confound the
relationship between site habitat conditions and resultant demographic
rates. Many waterbirds are highly dispersive and track ephemeral habitat
conditions at local, regional, or even continental scales (Cumming et
al., 2012, Pedler et al., 2014, Roshier et al., 2006), creating the
potential for mismatches between the scale of monitoring and the scale
at which demographic processes are governed. Habitat quality at a
particular wetland may be high relative to other points in time, yet
waterbirds do not capitalise on these favourable conditions because
there are other areas of high quality habitat in the landscape
(behavioural choice impacts) (Cumming et al., 2012). Consequently,
habitat quality assessments based on abundance, density, or occupancy
for the particular site may be decoupled from theoretical predictions if
data from the broader landscape are unavailable. The distribution of
many waterbird species is also influenced by social attraction (Gawlik
and Crozier, 2007). As a result, areas of high quality habitat may go
unused because waterbirds newly arriving in an area are drawn to sites
with existing waterbird presence (Gawlik and Crozier, 2007).
Many waterbirds are also migratory. Consequently, demographic parameters
in one part of the range may be decoupled from the habitat conditions
experienced at that time due to carry-over effects from previous seasons
(Aharon-Rotman et al., 2016a, Sedinger and Alisauskas, 2014, Swift et
al., 2020). For example, survival during the breeding period and
breeding success may be higher in individuals that depart their
non-breeding grounds in better condition (Swift et al., 2020).
Furthermore, breeding performance in one part of the range may influence
parameters including abundance and population age structure on the
non-breeding grounds, irrespective of the local conditions on the
non-breeding grounds (Rogers and Gosbell, 2006). In addition to
carryover effects, survival data may be particularly sensitive to pinch
points of low-quality habitat along the migratory flyway (Piersma et
al., 2016, Studds et al., 2017).
Due to the difficulties of obtaining waterbird demographic data in a
given area, an array of methods have been used as proxies to measure
habitat quality (Ma et al., 2010). The use of proxies also helps to
overcome budget limitations of management agencies by allowing snapshot
estimates of habitat quality to be made without the need for extended
periods of data collection in space and time (Osborn et al., 2017).
However, the many different options available for measuring habitat
quality can be bewildering for research scientists and conservation
practitioners (Pidgeon et al., 2006). There is little consensus on which
method, or combination of methods, produces the most meaningful estimate
of waterbird habitat quality, and in some cases, it is unclear as to
whether particular proxies meaningfully reflect underlying habitat
quality from the perspective of direct impact on population processes
(Johnson, 2005, Johnson, 2007, Van Horne, 1983). For example, density of
individuals may not reflect underlying habitat quality if the population
does not follow the ideal free distribution (Van Horne, 1983), and time
spent foraging may not reflect underlying habitat quality if individuals
are constrained by prey handling time or digestive bottlenecks (Van Gils
et al., 2005). Furthermore, the spatial scale at which proxies are
measured may have implications for their relevance to managers (Pidgeon
et al., 2006, Stephens et al., 2015).
In
this review, we seek to catalogue the methods that have been used to
quantify waterbird habitat quality and provide a synthesis of the
conditions under which each may provide meaningful measures of habitat
quality in future waterbird studies. Outputs from this review are
intended to guide environmental managers on the types of data they
should be collecting when attempting to quantify waterbird habitat
quality. This will ensure that decisions on how to manage habitat to
optimise habitat quality are based on meaningful information.