3.1 Methods of habitat quality
assessment
Measuring demographic
parameters
By definition, the quantification of habitat quality depends on
estimating a site’s contribution to survival and reproduction.
Therefore, any method that directly measures one or both of these
parameters will be free from error propagation caused by imperfect
correlation between a measured attribute and these variables. However,
cautious interpretation is still required when only one of these
attributes is measured, because sites with similar reproductive output
can have divergent population trajectories if the population size is
governed by adult survival, and vice versa (Cohen et al., 2009).
Similarly, emigration or immigration at a site may also obscure the
signal arising from measures of reproduction and survival (Cohen et al.,
2009). Measuring demographic rates can be a lengthy, costly, and
logistically challenging process. In waterbird research, directly
measuring a site’s contribution to survival and reproduction may be
unachievable owing to the mobility of waterbird populations. Although
our structured search returned examples of studies that did quantify
survival (e.g., Alves et al., 2013, Rice et al., 2007, Swift et al.,
2020) and/or reproduction (e.g., Hunt et al., 2017, Powell and Powell,
1986, Swift et al., 2020), most studies used proxies for one or both of
these measures.
Estimating food abundance and
availability
Many of the proxies in the reviewed studies assumed that a high-quality
habitat provided waterbird individuals with a high net energy intake
rate. The corollary assumption was that high net energy intake results
in increased survival and reproductive performance. Methods used to
infer net energy intake rate included measures of prey abundance, prey
accessibility, and waterbird physiology or morphology as an indicator of
past foraging returns (Table 1). Habitat quality assessments that are
based on habitat attributes are appealing because results are
independent of variation in bird behaviour caused by factors unrelated
to local habitat quality (e.g., current wind and rain conditions can
determine which sites waterbirds use at very local scales (Kelly, 2001)
and these short term changes are not typically useful for managers). For
this reason, measuring the abundance or biomass of food was used widely
in the reviewed studies to assess waterbird habitat quality. There is
support for this method being an appropriate proxy for habitat quality
because waterbirds preferentially forage at sites with the highest prey
biomass and density (Guerra et al., 2016, Rose and Nol, 2010). Moreover,
prey availability has a positive influence on reproductive performance
and survival (Herring et al., 2010, Holopainen et al., 2014, Swift et
al., 2020). However, there are also situations where prey biomass at a
site can be a poor indicator of habitat quality. For example, sites with
high prey biomass are not always favoured foraging sites (Hagy and
Kaminski, 2015), and although these sites might have high occupancy,
they do not necessarily support high waterbird abundance (Gillespie and
Fontaine, 2017). This suggests that factors such as predation risk,
forager condition, and prey accessibility modulate the effect of prey
biomass on habitat quality (Hagy and Kaminski, 2015). Such a
relationship is also dependent on waterbirds having a perfect knowledge
of the distribution of prey resources (Reurink et al., 2015), which may
not always be the case (Lewis et al., 2010), and relies on researchers
correctly identifying dietary preferences and requirements of focal
species.
The presence of suitable water levels and variation in water levels was
also used as a proxy for habitat quality in the reviewed studies. These
habitat attributes can influence accessibility of prey and foraging
energetics (Ma et al., 2010). In some cases only a small proportion of a
wetland provides suitable water levels for waterbirds to access prey
(Collazo et al., 2002). This suggests that there is value in quantifying
either prey biomass or the amount of suitable habitat through water
level measurements. However, the two attributes will interact to
influence the net rate of energy intake possible at a site meaning
studies that measure both variables may have a greater likelihood of
teasing apart meaningful habitat quality relationships and informing
appropriate management (Herring and Gawlik, 2013). Similarly, remotely
sensed measures of primary productivity (e.g., NDVI) are expected to be
correlated with prey abundance. Yet, the relationship between net energy
intake rate and primary productivity is dependent on changes in primary
productivity causing changes in prey abundance (e.g., invertebrates,
seeds, tubers) as well as those prey items being available to feeding
waterbirds (Guan et al., 2016, Zhang et al., 2017). This suggests there
is a hierarchy in the ability of proxies to provide precise habitat
quality estimates based on how direct the link between the variable
being measured and net energy intake rates is (Figure 1).
Estimating food intake
rate
The behaviour and habitat use patterns of waterbirds themselves were
often used in the reviewed studies to infer underlying patterns of
habitat quality (Table 1). Indicators of prey intake rate (be it
current, past or expected future foraging returns) were frequently used
metrics of habitat quality. Variables including peck rate, capture
success rate, and the proportion of time a bird spent foraging were
commonly measured to assess the current rate of energy intake supported
by a habitat. Defecation rate is significantly correlated with peck rate
in a visually foraging shorebird, supporting the assumption that peck
rate represents a valid indicator of intake rate (Rose and Nol, 2010).
Likewise, sites with a higher peck rate or probe rate had a higher rate
of successful prey captures in a study where capture success could be
visually verified (Kuwae et al., 2010). However, different prey items
have different energy content and different processing costs within the
digestive system (Dugger et al., 2007, Jorde et al., 1995). This means
that the net rate of energy intake will depend on the prey type
consumed. This may not be an issue in studies of diet specialists, but
it may confound the interpretation of peck rate and capture success data
for diet generalists. In situations where the diet of the population
being studied is not well understood, investigating the prey community
composition to determine prey encounter rates, or dietary studies (e.g.,
metabarcoding of prey DNA sequences in faecal samples) will inform
whether differences in peck rate between sites or across time genuinely
reflect changes in energy returns.
Intake rates over the recent and more distant past were inferred from a
variety of variables including body condition, blood metabolites, and
indicators of feather growth rate. These have the advantage that they
reflect assimilated energy rather than gross intake including energy
lost via excretion or through processing costs. However, the longer
timeframe of integration meant that studies using these methods were
rarely site-specific, rather they tended to assess habitat quality at
regional scales (e.g., Aharon-Rotman et al., 2016b). In cases where
individuals use only a small geographic area (e.g., when nesting
constrains movements, or individuals have strong residency patterns)
these measures may provide insights into site-specific habitat quality.
For example, Swift et al. (2020) found that visually-scored body
condition of non-breeding Hudsonian Godwits Limosa haemastica was
correlated with pecking rate at individual non-breeding sites. This
suggests that these birds were resident at sites long enough to
integrate site-specific habitat quality information in the form of body
condition. Importantly, birds with higher body condition had higher
survival and reproductive output the following breeding season,
indicating that body condition reliably influenced demographic rates
(Swift et al., 2020).
Predation pressure
Given the direct link between predation pressure and survival, it was
surprising that predation pressure was estimated relatively infrequently
in the reviewed studies. This is perhaps reflective of the difficulties
of censusing predator populations due to predators of waterbirds
typically occurring at low density and predation events on adult
waterbirds being rare. Where predation pressure was quantified, these
studies often focused on nest predation (e.g., Kenow et al., 2009,
Pehlak and Lõhmus, 2008, Trinder et al., 2009). Most studies that
inferred an influence of predation pressure on habitat quality assumed
that the abundance of predators was correlated with predation rate
without explicitly testing this assumption, which may be problematic
when generalist predators are involved. Some studies also assessed
predation pressure by using vigilance or escape behaviours of waterbirds
(Fernández and Lank, 2010, Gunness et al., 2001). This has the advantage
of integrating information on the degree of lost foraging time as a
result of predation pressure because lost foraging opportunities will
affect reproductive performance as well as survival (Castillo-Guerrero
et al., 2009).
Physical habitat
attributes
Many of the reviewed studies measured various physical and/or chemical
attributes of waterbird habitats to infer habitat quality. The
attributes measured were purported to influence habitat quality via
their contribution to supporting viable prey populations (e.g., water
pH, water conductivity, sediment grain size), enabling access to
sufficient quantities of food (e.g., water area, pond density in the
local area and vegetation composition, as well as water level which we
discussed previously), or providing shelter from predators (e.g.,
vegetation structure). In most cases, these environment attributes are
linked indirectly to demographic rates (Figure 1) and the mechanisms
governing their effects may be difficult to disentangle (Raquel et al.,
2016). Nonetheless, physical attributes of the habitat may provide
waterbirds with visual cues as to the quality of a site and play a role
in determining patterns of site use, which can have flow-on effects on
demographic rates (Buderman et al., 2020).
Other methods
A variety of other methods were used infrequently in the reviewed
studies (Table 1). These included estimates of levels of human
disturbance, individual movement data (e.g., home range size), and the
spatial distribution of individuals in different age classes. Despite
their infrequent use, these methods may provide meaningful habitat
quality information. Factors such as the cost of obtaining the data or
the difficulty of obtaining the data (e.g., challenges distinguishing
between age classes in the field) probably contributed to their
infrequent use.
Combination of methods
Many of the reviewed studies recorded data on multiple proxies for
habitat quality. Multiple lines of evidence allowed researchers to tease
apart complex relationships among various parameters in their respective
study systems and provide powerful insight to conservation managers
(Cohen et al., 2009, Hunt et al., 2017, Swift et al., 2020). In these
studies, it was often possible to pinpoint factors that were limiting
habitat quality, providing managers with priorities to address in order
to improve habitat quality. For example, Cohen et al. (2009) recommended
that restoring Piping Plover, Charadrius melodus, habitat
adjacent to bayside intertidal flats would improve habitat quality by
increasing the number of breeding pairs that could occupy a site.
However, this action must be carried out in conjunction with predator
management in order to achieve the desired increase in reproductive
output.
Factors influencing the choice of variables to
measure
Staying within the project’s
scope
Our synthesis of the habitat quality literature indicates that there is
a hierarchy of data quality from directly monitoring demographic rates
to measuring parameters that are increasingly indirectly linked to
demography. Yet, practitioners typically face a trade-off between the
need for accuracy of the habitat quality estimate and their particular
study’s aims and constraints. If it is feasible, measuring demographic
rates directly generally involves extended field time, individually
marked birds, limited spatial scale, and substantial costs (Buderman et
al., 2020). Other factors may also influence the suitability of a proxy
for the habitat quality assessment at hand including ethical
considerations (Hunt et al., 2013), and the availability of
appropriately trained personnel. Physiological and morphological
measurements used in the reviewed studies typically required birds to be
handled (but see the abdominal profile index method; Swift et al.,
2020), which imposes stress on the study subjects (Karlíková et al.,
2018), and capturing a large sample size of birds can be time-consuming.
This may mean that methods requiring birds to be handled, including
individually marking birds for quantifying demographic rates, are not
feasible within the scope of a project.
Spatial and temporal scales of
assessments
Another consideration that must be made prior to implementing a study on
habitat quality is whether the habitat quality measure being used
returns data at a relevant spatial and/or temporal scale. For example,
prey abundance measures typically provide very local scale (both spatial
and temporal) information on habitat conditions, but may not be
representative of habitat quality across the entire wetland or extended
timeframes (e.g., the entire non-breeding period). For example, Fonseca
and Navedo (2020) reported a 43% reduction in invertebrate prey biomass
as a result of shorebird foraging in study plots over the course of
three days. Consequently, habitat quality assessments either side of
this three-day period could yield vastly different inferences about
local habitat quality and neither may be representative of habitat
quality over an extended timeframe. The accuracy of these methods in
terms of returning habitat quality data at time-scales meaningful for
management will therefore be increased by repeated sampling (Murray et
al., 2010). This was reflected in a number of the reviewed studies,
especially those aimed at specifying management regimes, repeating
sampling both spatially, and intra- and inter-annually (e.g., Gillespie
and Fontaine, 2017). Whereas methods that relied on measuring attributes
of the habitat typically provided snapshot estimates of habitat quality,
methods reliant on waterbird body condition or physiology (e.g.,
abdominal profile index or red blood cell heat shock protein
concentrations) often provide information integrated over longer
timeframes (Herring and Gawlik, 2013). They may therefore be unsuitable
for site-specific and/or instantaneous habitat quality questions, but
may be applied to questions informing management of a regional wetland
complex over broader timeframes. Similarly, remotely sensed measures of
primary productivity offer the potential to rapidly and cost-effectively
monitor habitat conditions at large spatial and temporal scales. For
example, Wen et al. (2016) used remotely sensed primary productivity
data to inform an assessment of waterbird habitat quality across a
810,000 km2 study area in multiple years.
There is no rule that governs whether the spatial or temporal scale of a
particular proxy is appropriate for a particular application because
even labour-intensive or costly methods that return site-specific
information may be suitable for large-scale projects if the budget
enables sufficiently widespread sampling (e.g., sites and time points).
We provide some recommendations as to the spatial and temporal scales
that methods for habitat quality assessments are typically carried out
at (Table 1). Readers may also find papers such as Behney and
colleagues’ (2014) guide to determining the optimum number of benthic
core samples to collect useful for planning how much field effort is
likely to be involved when planning a sampling regime.
What makes for a good habitat quality
assessment?
Measuring habitat quality enables conservation managers to assess the
need for or effectiveness of management actions (e.g., Schultz et al.,
2020). The ultimate objective of conservation management is to influence
demographic parameters of conservation targets to improve conservation
status. Therefore, assessments of habitat quality inherently must
determine a site’s contribution to survival probability and/or
reproductive output. This requires there to be a link between the
variable, or combination of variables, used to measure habitat quality
and demographic rates (Figure 1). Before commencing an assessment of
habitat quality, the researcher must carefully consider whether the
selected measure does actually influence demographic rates. For example,
quantifying the time budgets of waterbirds is a commonly used method for
inferring differences in habitat quality (Dugger and Feddersen, 2009,
van der Kolk et al., 2019). However, the inferences derived from time
budget comparisons may not actually reflect changes in underlying
habitat quality. Time budgets can be flexible to buffer intrinsic
changes in requirements (Mallory et al., 1999). For example, this may be
due to individuals dedicating more time to foraging to meet the
metabolic demands of producing a clutch of eggs (Mallory et al., 1999),
or dedicating more time to feeding to fatten up for migration
(Castillo-Guerrero et al., 2009). That is not to say that time budgets
are unsuitable for quantifying habitat quality, but care must be taken
to ensure that appropriate comparison groups are being used (e.g.,
sampling at the same time of year).
Researchers must also be aware that inferences made about populations
that are not at equilibrium may depart from theoretical relationships
underpinning many habitat quality proxies. For example, populations that
have been reduced below carrying capacity by historical or offsite
factors may not show any temporal differences in various local habitat
quality proxies (e.g., foraging success, stress markers, body condition,
and time budgets) because individuals are easily able to meet their
resource requirements even if local habitat quality is declining.
Similarly, there may be differences in the relevance of some habitat
quality proxies depending on whether the conservation target is a
resident population, or a dispersive or migratory population (Loewenthal
et al., 2015). Abundance and density are clearly linked to local habitat
quality for resident populations, but may not be truly reflective of
local habitat quality for populations that undertake large-scale
movements exposing individuals to factors that limit population size
elsewhere in the range. For example, Jia et al. (2018) reported declines
in abundance of migratory shorebirds at a migratory staging site, but
none of the measured proxy variables for habitat quality could explain
these declines. They suggest that factors in other parts of the
migratory range may be responsible for driving the observed declines in
abundance rather than changes in habitat quality at their study site.
Many of the habitat quality proxies identified in this review assume
individuals have perfect knowledge of the resource distribution at a
site and behave such that the net rate of energy gain is being maximised
at any given time (Reurink et al., 2015). Several factors can result in
waterbirds using their habitat in ways that do not conform to these
assumptions. The choice of foraging site for many waterbirds is strongly
influenced by conspecific attraction (Gawlik and Crozier, 2007, Herring
et al., 2015, Smith, 1995). This is also true for the selection of nest
sites (Sebastián-González et al., 2010c). Furthermore, fidelity to areas
that have provided favourable habitat conditions in the past may
decouple patterns of waterbird habitat use from current habitat
conditions (O’Neil et al., 2014). Waterbird habitat requirements may
also change with breeding stage (Holopainen et al., 2014), and during
less energetically demanding parts of the annual cycle, such as the
non-breeding period, individuals may be less selective in their habitat
use decisions (Sebastián-González et al., 2010b).
Most of the reviewed studies provided a relative assessment of habitat
quality (i.e., they compared waterbird habitat quality at a site to
previous points in time, or made comparisons between sites). These
studies allow researchers to determine habitat quality trends or
identify the best and worst sites in a landscape, but do not enable
managers to determine whether the habitat quality is sufficient to
maintain viable waterbird populations. There were some studies that
sought to determine whether the habitat quality at a site was sufficient
to support population growth or whether the site represented a sink
habitat (e.g., Roy et al., 2019, Sabatier et al., 2010). These studies
do enable managers to determine whether management intervention is
necessary rather than arbitrarily setting a reference site as the
standard against which to decide whether management is warranted. In
particular, studies seeking to identify whether a site had sufficient
habitat quality to support population growth tended to focus directly on
reproductive output or survival data (Roy et al., 2019, Weiser et al.,
2018), or in some cases focused on energetic demands relative to prey
resources (West et al., 2005).
Together, the potentially confounding factors mean that there is no
universally applicable habitat quality proxy. Yet, with careful
consideration and a detailed understanding of the ecology of the study
system, waterbird researchers and management practitioners can derive
meaningful measures of habitat quality.