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\begin{document}

\title{Measuring habitat quality for waterbirds: a review}



\author[1]{Rowan Mott}%
\author[1]{Thomas Prowse}%
\author[1]{Micha Jackson}%
\author[2]{Daniel Rogers}%
\author[2]{Jody O'Connor}%
\author[1]{Justin Brookes}%
\author[1]{Phillip Cassey}%
\affil[1]{The University of Adelaide}%
\affil[2]{Department for Environment and Water}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Quantifying habitat quality is dependent on measuring a site's relative
contribution to population growth rate. This is challenging for studies
of waterbirds, whose high mobility can decouple demographic rates from
local habitat conditions and make sustained monitoring of individuals
near-impossible. To overcome these challenges, biologists have used many
direct and indirect proxies of waterbird habitat quality. However,
consensus on what methods are most appropriate for a given scenario is
lacking. We undertook a structured literature review of the methods used
to quantify waterbird habitat quality, and provide a synthesis of the
context-dependent strengths and limitations of those methods. Our
structured search of the Web of Science database returned a sample of
398 studies, upon which our review was based. The reviewed studies
assessed habitat quality by either measuring habitat attributes (e.g.,
food abundance, water quality, vegetation structure), or measuring
attributes of the waterbirds themselves (e.g., demographic parameters,
body condition, behaviour, distribution). Measuring habitat attributes,
although they are only indirectly related to demographic rates, has the
advantage of being unaffected by waterbird behavioural stochasticity.
Conversely, waterbird-derived measures (e.g., body condition, peck
rates) may be more directly related to demographic rates than habitat
variables, but may be subject to greater stochastic variation (e.g.,
behavioural change due to presence of conspecifics). Therefore, caution
is needed to ensure that the measured variable does influence waterbird
demographic rates. This assumption was usually based on ecological
theory rather than empirical evidence. Our review highlighted that there
is no single best, universally applicable method to quantify waterbird
habitat quality. Individual project specifics (e.g., time frame, spatial
scale, funding) will influence the choice of variables measured. Where
possible, practitioners should measure variables most directly related
to demographic rates. Generally, measuring multiple variables yields a
better chance of accurately capturing the relationship between habitat
characteristics and demographic rates.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{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 (P\selectlanguage{ngerman}érot 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.

\section*{Methods}

{\label{methods}}

For the purposes of this review, we followed the definition of
waterbirds used by Wetlands International (2012). This covers all
species within 32 bird families that are ecologically dependent on
wetlands. The most familiar of these families are the Anatidae (ducks,
geese and swans), Laridae (gulls and terns), Ardeidae (herons and
egrets), Scolopacidae (sandpipers), and Charadriidae (plovers). Other
representatives include the Rallidae (rails and crakes), Podicipedidae
(grebes), Threskiornithidae (ibises and spoonbills), and
Recurvirostridae (stilts and avocets).

Systematic reviews require defining the question elements `subject',
`intervention', `outcome', and `comparator' (Pullin and Stewart, 2006).
The diverse nature of the waterbird habitat quality literature meant
that many waterbird habitat quality studies lack one or more of these
elements (e.g., most studies are descriptive rather than measuring the
outcome of a management intervention relative to a control case).
Consequently, conducting a formal meta-analysis was not possible. Hence,
we used the `narrative synthesis' approach recommended by Haddaway et
al. (2020) for synthesising heterogeneous literature. To obtain a
representative sample of the literature for synthesis, we used a
structured approach to identify relevant information sources (published
literature, reports, and grey literature) and use these sources to make
qualitative assessments of the various methods that have been used for
measuring waterbird habitat quality.

We searched the Web of Science (all databases) on 17 December 2020 to
obtain a set of papers on which to base this review. The following
search string, in which TS means `Topic Search', was used:

TS = (waterbird* OR shorebird* OR wader* OR ''wading bird*'' OR
waterfowl) AND TS = (''habitat quality'' OR ''habitat condition'' OR
''environment* quality'' OR ''environment* condition'' OR ''wetland
quality'' OR ''wetland condition'')

This returned 411 search results (398 after removing duplicates) upon
which the following synthesis is based (See Table S1 for list of
returned results).

\section*{Synthesis of reviewed
studies}

{\label{synthesis-of-reviewed-studies}}

Our structured search returned studies that undertook waterbird habitat
quality assessments in two main ways: studies that measured some
biophysical attribute(s) of the habitat; and studies that measured some
attribute(s) of waterbirds themselves to infer underlying habitat
quality (Table 1). Studies that measured attributes of waterbirds
themselves could be further broken down into four sub-categories:
studies that directly measured waterbird demographic characteristics;
studies that measured waterbird body condition; studies that measured
waterbird behaviour; and studies that measured waterbird distribution
(Table 1). There were also studies that used methods from a combination
of these categories.

\par\null

Table 1. Catalogue of methods used to assess waterbird habitat quality
in studies reviewed as part of the structured literature review. For
each method, examples of studies that used the method are given along
with an indication of the support or lack thereof for the given method.
A `---' symbol in the Supporting evidence and contradictory evidence
columns indicates that no data for these cells were found in the
reviewed papers. The spatial (site, region, flyway) and temporal
(instantaneous, within-season, annual) scales that data collection
pertains to are also given.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Method\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Metrics\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Supporting evidence\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Contradictory evidence\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Relevant spatial and temporal scales\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Direct habitat measures}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Food availability\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Prey animal biomass (Ati\selectlanguage{ngerman}énzar et al., 2012, Deboelpaep et al., 2020,
Herring and Gawlik, 2013, Holopainen et al., 2014, Hunt et al., 2017,
Parks et al., 2016, Schultz et al., 2020) Plant-derived food density or
abundance (Arzel et al., 2015, Atiénzar et al., 2012, Dugger and
Feddersen, 2009)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Birds behaviourally track sites with highest prey biomass and density
(Rose and Nol, 2010) Prey availability has a positive influence on
reproductive performance (Herring et al., 2010) Chick condition is
related to local prey abundance (Hunt et al., 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Predicts occupancy but not abundance (Gillespie and Fontaine, 2017)
Sites with high food densities are not always the favoured foraging
sites (Hagy and Kaminski, 2015) The seeds of different plant species
consumed by waterfowl have different energy content (Dugger et al.,
2007) Different food items can result in different mass gain even when
fed \emph{ad libitum} (Jorde et al., 1995) Waterbirds may forage
selectively on larger size-class prey items meaning that overall prey
density is not reduced through waterbird foraging even though
waterbirds' preferred prey size has been significantly depleted (Fonseca
and Navedo, 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Primary productivity\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Normalised Difference Vegetation Index (NDVI) (Tang et al., 2016, Zhang
et al., 2017) Enhanced Vegetation Index (EVI) (Guan et al., 2016)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
The method provides an indirect indication of habitat quality with at
least one further transitional state before primary productivity
influences waterbird energy intake rate (Zhang et al., 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region/Flyway -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Predation pressure\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Predator track density (Cohen et al., 2009) Index of predator
reproduction (Trinder et al., 2009) Proportion of radio-tracked
individuals predated (Kenow et al., 2009, Swift et al., 2020) Proportion
of real or fake nests predated (Pehlak and L\selectlanguage{ngerman}õhmus, 2008, Swift et al.,
2020) Alternate prey density (Holopainen et al., 2014)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Predation can be the leading cause of waterbird nest failure (Riecke et
al., 2019) Predation risk is evaluated by waterbirds and trade-offs made
that may reduce other components of fitness (e.g., foraging rate)
(Fern\selectlanguage{ngerman}ández and Lank, 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Nest predation rate was not a function of predator abundance or the
availability of alternate prey species (Mach\selectlanguage{ngerman}ín et al., 2019) The
influence of predation can differ depending of the waterbird population
density (Lebeuf and Giroux, 2014)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Vegetation structure\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Vegetation height (Barati et al., 2011) Vegetation cover/abundance
(Ati\selectlanguage{ngerman}énzar et al., 2012, Hamza et al., 2015, Hierl et al., 2007, Nyman
and Chabreck, 1996) Vegetation community composition (Benedict and Hepp,
2000, Dugger and Feddersen, 2009) Presence of invasive plants (Khan,
2010, Tavernia and Reed, 2012)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Vegetation structure has implications for the suitability of a site for
nest placement (Barati et al., 2011)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Dense vegetation may increase prey abundance but reduce prey capture
efficiency (Lantz et al., 2011)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Wetland spatial attributes\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Connectivity to neighbouring wetlands (Sebasti\selectlanguage{ngerman}án-González et al., 2010b)
Pond area (Atiénzar et al., 2012, He et al., 2009, Merendino and Ankney,
1994) Shoreline irregularity (Merendino and Ankney, 1994)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Pond size and distance to the nearest neighbouring wetland are important
determinants of waterbird habitat selection (Sebasti\selectlanguage{ngerman}án-González et al.,
2010b)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Cycles of hydrological stress (drought/non-drought) can influence
waterfowl habitat preferences, with birds seeking relatively deeper
water bodies during drought irrespective of other habitat variables that
are influential in wet years (Ati\selectlanguage{ngerman}énzar et al., 2012)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Water level\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Drawdown (Herring and Gawlik, 2013, Townsend et al., 2006); Water level
variability (Collazo et al., 2002) Availability of shallow water
(Collazo et al., 2002, Gawlik and Crozier, 2007, Lantz et al., 2011)
Landscape depth heterogeneity (Beerens et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Wading birds preferentially selected ponds that had been experimentally
manipulated to have shallow rather than deep water (Gawlik and Crozier,
2007) and waterbird species richness and density correlates with the
availability of shallow water habitats (Wang and So, 2003) Water level
recession rate was a key influence on physiological condition of two
species of waterbirds (Herring and Gawlik, 2013)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Water level variability did not influence habitat selection of wading
birds (Gawlik and Crozier, 2007)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Disturbance\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Distance to footpaths, roads, or railways (Burton et al., 2002, Hu et
al., 2016, Li et al., 2019) Human settlements (Li et al., 2019)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
The presence of people and vehicles nearby ([?]50 m) reduces foraging
rates (Maslo et al., 2012) Likewise, time spent foraging and flock
density were reduced at a highly disturbed site (Swift et al.,
2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Human activities (e.g., clam harvesting) may have positive effects on
waterbirds, especially shorebirds (Hamza et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Foraging substrate\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Sediment grain size (Reurink et al., 2015, Rose and Nol, 2010) Organic
carbon content (Hamza et al., 2015, Reurink et al., 2015) Mud content
(Hamza et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Prey biomass is strongly predicted by physical environment conditions
including organic content and particle sizes of the sediments (Rose and
Nol, 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Land use\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Proportion of agricultural land use (Austin et al., 2001, Duncan et al.,
1999) Mariculture (Li et al., 2019) Mining (Li et al., 2019)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Changing land use can cause ecological traps if agricultural landscapes
appear similar to natural landscapes (e.g., grasslands) but offer lower
habitat quality (Buderman et al., 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Factors such as traditional site use by waterbirds can confound the
signal of change in response to changing land use (Tombre et al.,
2005)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Water chemistry\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Colour/turbidity (Ati\selectlanguage{ngerman}énzar et al., 2012, Merendino and Ankney, 1994) pH
(Merendino and Ankney, 1994, Walsh et al., 2006) Conductivity/salinity
(Atiénzar et al., 2012, Merendino and Ankney, 1994) Dissolved nutrients
(Merendino and Ankney, 1994, Pöysä et al., 2001, Walsh et al., 2006)
Chlorophyll-\selectlanguage{greek}α \selectlanguage{english}concentration (Ati\selectlanguage{ngerman}énzar et al., 2012)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Prey biomass is influenced by salinity (Rose and Nol, 2010) Water
chemistry variables including pH, salinity, and nitrogen and potassium
concentration can be a predictor of occurrence of breeding ducks (Walsh
et al., 2006)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Bird-derived estimates}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Demographic measures}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Reproduction\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Clutch size/volume (Hunt et al., 2017, Mallory et al., 1994, Powell and
Powell, 1986) Number of fledglings (Powell and Powell, 1986)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
A direct contributor to the per capita rate of population increase, the
most proximate indicator of habitat quality\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Survival\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Adult survival (Alves et al., 2013, Rice et al., 2007, Swift et al.,
2020) Brood survival (Aubry et al., 2013, Cohen et al., 2009, Hunt et
al., 2017, Owen and Pierce, 2014, Simpson et al., 2007, Swift et al.,
2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
A direct contributor to the per capita rate of population increase, the
most proximate indicator of habitat quality\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Distributional measures}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Density or abundance\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Abundance (Castillo-Guerrero et al., 2009, Dugger and Feddersen, 2009,
Ganzevles and Bredenbeek, 2005, Hickman, 1994, Liu et al., 2006) Species
richness (Dugger and Feddersen, 2009, Hickman, 1994) Density (Loewenthal
et al., 2015, Swift et al., 2020) Abundance of breeding pairs (Arzel et
al., 2015, Austin et al., 2001, Sebasti\selectlanguage{ngerman}án-González et al., 2010a)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
The density of breeding pairs increased much faster than could be
explained by population growth rates following habitat management that
resulted in greater food availability (Loewenthal et al., 2015) This was
attributed to previously subordinate adults taking up breeding
territories as territory size of existing pairs contracted (Loewenthal
et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Can be confounded by site fidelity (O'Neil et al., 2014), lags in
response to change in condition (Loewenthal et al., 2015, Meltofte,
2006), dispersal barriers or costs, and imperfect knowledge of habitat
(Lewis et al., 2010) Local and regional weather influences habitat use
(Kelly, 2001, Schummer et al., 2010) Reproductive output is not
correlated with population density (Cohen et al., 2009) Reduction in
food availability can increase shorebird density as they are
concentrated into the remaining suitable patches (Kosztol\selectlanguage{ngerman}ányi et al.,
2006) Disturbance by human activity and farming rather than habitat
quality (availability of foraging areas) more strongly influences
waterbird species richness and abundance (Quan et al., 2002) Requires
birds to correctly perceive habitat cues, which may not always be the
case (e.g., agricultural land uses may resemble native grasslands, but
have much lower reproductive output) (Buderman et al., 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Phenology\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Length of breeding period (Raquel et al., 2016) Residence times on
non-breeding or stopover sites (O'Neal et al., 2012, Rice et al., 2007,
Williams et al., 2019)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Spring migration stopover duration can decrease as a function of Julian
day of the year (Williams et al., 2019)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region --Within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Age class distribution\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Age class distribution (Fern\selectlanguage{ngerman}ández and Lank, 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Adult shorebirds occupy sites with greater prey availability and lower
predation risk than immature birds (Fern\selectlanguage{ngerman}ández and Lank, 2006)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Hunting records\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Harvest numbers as an indicator of present and past habitat quality
(Merendino et al., 1992)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Region -- Annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Individual condition measures}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Morphological variables\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Abdominal profile index (Swift et al., 2020) Body mass (Herring and
Gawlik, 2013, Hunt et al., 2017) Body condition index (Aubry et al.,
2013, Parks et al., 2016) Chick growth rate (Hunt et al., 2017, Owen and
Pierce, 2014)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Abdominal profile index on the non-breeding grounds was correlated with
breeding ground return rates, and subsequent nest survival and chick
fate (Swift et al., 2020) Chick growth rates and adult body mass were
positively correlated with invertebrate abundance in breeding Piping
Plovers (Hunt et al., 2017)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region --Within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Physiological variables\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Stress markers (Aharon-Rotman et al., 2016b, Herring and Gawlik, 2013,
Thomas and Swanson, 2013) Immune response markers (Buehler et al., 2009)
Foraging metabolites (Lyons et al., 2008, Thomas and Swanson,
2013)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Birds that occupy sites with higher fueling rates have lower
concentration of physiological markers of stress in their blood
(Aharon-Rotman et al., 2016b)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Different species with different foraging strategies can have different
blood physiology responses to changing availability of prey (Herring and
Gawlik, 2013)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region --Within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Parasite burden\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Intestinal helminth load (Conner England et al., 2018) Haemosporidian
parasite infection (Aharon-Rotman et al., 2016b)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Parasite burden negatively correlated with foraging habitat quality for
some parasite taxa, but not significantly for all parasite taxa (Conner
England et al., 2018)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region --Within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Ptilochronology\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Feather growth rate (Swift et al., 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Width of feather growth bands was positively correlated with an index of
body condition (abdominal profile index) and feeding rates (Swift et
al., 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region --Within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\emph{Behavioural measures}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Foraging parameters\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Peck/probe rate (Castillo-Guerrero et al., 2009, Mander et al., 2013)
Success rate (Castillo-Guerrero et al., 2009, Swift et al., 2020) Step
rate during foraging (Mander et al., 2013) Energy intake rate (Yu et
al., 2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Positively correlated with prey density and biomass and at productive
sites may not be affected by interference competition (Rose and Nol,
2010) Peck rate is correlated with defecation rate indicating that peck
rate is a meaningful proxy for intake rate (Rose and Nol, 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Capture success can be influenced by conspecifics, with increases in
capture success occurring until conspecific density becomes high enough
to induce interference competition (Stolen et al., 2012) Peck rate also
reaches an upper asymptote, so may not be a true indication of habitat
quality in very high productivity landscapes (Rose and Nol, 2010)
Pecking rate can be significantly higher than probing rate for an
equivalent energy return (Kuwae et al., 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Time budgets\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Proportion of time spent foraging (Castillo-Guerrero et al., 2009,
Dugger and Feddersen, 2009, van der Kolk et al., 2019) Proportion of
time in non-foraging behaviours (e.g., vigilance, disturbance)
(Castillo-Guerrero et al., 2009, Maslo et al., 2012, Yu et al.,
2020)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Oystercatchers that spent longer foraging had lower inferred survival
(van der Kolk et al., 2019)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Time budgets may vary within an individual period of the annual cycle
(e.g., between breeding stages, or within the non-breeding period)
(Castillo-Guerrero et al., 2009, Mallory et al., 1999) or due to the
presence of conspecifics (Kosztol\selectlanguage{ngerman}ányi et al., 2006, Mallory et al.,
1999)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Anti-predator behaviours\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Vigilance rates (Fern\selectlanguage{ngerman}ández and Lank, 2010) Flight initiation distance
(Gunness et al., 2001)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
At sites where vigilance rates were higher, waterbirds maintained lower
body mass (Fern\selectlanguage{ngerman}ández and Lank, 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Individual movements\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Home range size (Herring and Collazo, 2005) Commuting distance (Custer
et al., 2004)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
---\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Flight speeds\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Flight speeds between foraging patches (Reurink et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Birds fly faster when heading to patches of high prey abundance because
the greater expected returns are able to offset the greater flight costs
of choosing to fly faster (Reurink et al., 2015)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Requires the birds to have perfect knowledge of the resource
distribution available (Reurink et al., 2015), which may not always be
the case (Lewis et al., 2010)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Site/region -- Instantaneous/within season/annual\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\subsection*{3.1 Methods of habitat quality
assessment}

{\label{methods-of-habitat-quality-assessment}}

\subsubsection*{Measuring demographic
parameters}

{\label{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.

\subsubsection*{Estimating food abundance and
availability}

{\label{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).

\subsubsection*{Estimating food intake
rate}

{\label{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 \emph{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).

\subsubsection*{Predation pressure}

{\label{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 L\selectlanguage{ngerman}õhmus, 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).

\subsubsection*{Physical habitat
attributes}

{\label{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).

\subsubsection*{Other methods}

{\label{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.

\subsubsection*{Combination of methods}

{\label{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, \emph{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.

\subsection*{Factors influencing the choice of variables to
measure}

{\label{factors-influencing-the-choice-of-variables-to-measure}}

\subsubsection*{Staying within the project's
scope}

{\label{staying-within-the-projects-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.

\subsubsection*{Spatial and temporal scales of
assessments}

{\label{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 km\textsuperscript{2} 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.

\subsection*{What makes for a good habitat quality
assessment?}

{\label{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.

\section*{Conclusions}

{\label{conclusions}}

This review of the literature comprising almost 400 articles strongly
suggests that there is no one broadly accepted method for assessing
waterbird habitat quality. Directly measuring breeding success and
survival rate are the most reliable measures, but it is unfeasible to
obtain these data in many cases. A variety of proxy measures are
available, but their interpretation requires substantive
contextualisation and a good understanding of their appropriateness to a
specific project aim.

In general, if it is not possible to measure direct demographic
parameters, projects should consider the suite of available proxy
measures (Table 1) and consider which are most suitable to their site,
budget and timeframe. Often, developing a protocol based on multiple
proxies will increase confidence in results over the use of a single
proxy. For example, studies investigating the comparative habitat
quality of multiple sites could use a combination of waterbird
abundance, behaviour and body condition coupled with a measure of prey
availability to gain insight into which site(s) are providing better
food resources. Studies assessing if a single site is profitable for
waterbirds from an energy perspective (i.e. habitat quality is
sufficiently high to support population growth) could use a combination
of waterbird behaviour and available energy density to assess whether
daily energy requirements are being met at the site. All studies using
proxy measures should be mindful of the potential for interactions
between features of the habitat (e.g., prey abundance and prey
accessibility) to influence the direction of the relationship between
habitat conditions and resultant demographic rates.

\section*{Acknowledgments}

{\label{acknowledgments}}

This research was funded by the South Australian Government's Healthy
Coorong, Healthy Basin Program, which is jointly funded by the
Australian and South Australian governments. We thank our project
advisory committee and partners within the Goyder Institute for Water
Research and Department for Environment and Water for feedback on drafts
of this review.

\section*{Conflict of interest}

{\label{conflict-of-interest}}

The authors have no conflict of interest to declare.

\section*{Author contributions}

{\label{author-contributions}}

Funding acquisition was carried out by PC, JB, DR, JO and TP. RM and TP
were responsible for methodology. RM carried out data curation. RM, TP
and MJ undertook the investigation. All authors contributed to
conceptualisation of the study and writing, review and editing of the
original and subsequent drafts.

\section*{Data accessibility}

{\label{data-accessibility}}

Data are available in article appendix Table A1

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\section*{Appendix}

{\label{appendix}}

Table A1. List of the results returned by the Web of Science search
using the search string TS=(waterbird* OR shorebird* OR wader* OR
''wading bird*'' OR waterfowl) AND TS=(''habitat quality'' OR ''habitat
condition'' OR ''environment* quality'' OR ''environment* condition'' OR
''wetland quality'' OR ''wetland condition'').\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Citation} & \textbf{Source} & \textbf{Volume} &
\textbf{Pages}\tabularnewline
\midrule
\endhead
Buderman et al. 2020 & Journal of Animal Ecology & 89 &
1961-1977\tabularnewline
de Fouw et al. 2020 & Journal of Experimental Marine Biology and Ecology
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Deboelpaep et al. 2020 & Freshwater Biology & 65 &
1529-1542\tabularnewline
Fonseca \& Navedo 2020 & Journal of Environmental Management & 262 &
110290\tabularnewline
Gao et al. 2020 & Landscape Ecology & 36 & 179-190\tabularnewline
Gherardi-Fuentes et al. 2020 & Acta Oecologica-International Journal of
Ecology & 105 & 103560\tabularnewline
Hassab et al. 2020 & Alexandria Journal of Veterinary Sciences & 63 &
1-9\tabularnewline
Howell et al. 2020 & Ibis & 162 & 535-547\tabularnewline
Jia et al. 2020 & Ecological Indicators & 114 & 106330\tabularnewline
Moon et al. 2020 & Ocean and Polar Research & 42 & 21-31\tabularnewline
Mullins \& Craig 2020 & Ostrich & 91 & 182-187\tabularnewline
Patton et al. 2020 & Wetlands & 40 & 2041-2054\tabularnewline
Schultz et al. 2020 & Wetlands & 40 & 2025-2040\tabularnewline
Swift et al. 2020 & Journal of Animal Ecology & 89 &
2043-2055\tabularnewline
van der Kolk et al. 2020 & Behavioral Ecology & 31 &
371-382\tabularnewline
Wang et al. 2020 & Integrative Zoology & 15 & 595-602\tabularnewline
Yu et al. 2020 & Environmental Science and Pollution Research & 27 &
18843-18852\tabularnewline
Zhou et al. 2020 & Science of the Total Environment & 737 &
140190\tabularnewline
Ali et al. 2019 & International Journal of Environmental Science and
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Bai et al. 2019 & Huanjing Kexue Yanjiu & 32 & 1001-1011\tabularnewline
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Chambon et al. 2019 & Animal Behaviour & 149 & 23-32\tabularnewline
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Choudhury et al. 2019 & Environment and Ecology & 37 &
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Jia et al. 2019 & Sustainability & 11 & 6814\tabularnewline
Li et al. 2019 & Wetlands & 39 & 67-77\tabularnewline
Liu et al. 2019 & Environmental Science and Pollution Research & 27 &
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Liu et al. 2019 & Bulletin of Environmental Contamination and Toxicology
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Machin et al. 2019 & Polar Biology & 42 & 1571-1579\tabularnewline
Otto et al. 2019 & United States Geological Survey & NA &
https://doi.org/10.5066/P9O61BCB\tabularnewline
Pierce et al. 2019 & Wilson Journal of Ornithology & 131 &
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Raquel et al. 2019 & Oecologia & 191 & 217-229\tabularnewline
Riecke et al. 2019 & Waterbirds & 42 & 261-271\tabularnewline
Roy et al. 2019 & Journal of Wildlife Management & 83 &
1720-1734\tabularnewline
Sedinger et al. 2019 & Ecology and Evolution & 9 &
12701-12709\tabularnewline
Tangen et al. 2019 & U S Geological Survey Open-File Report & 1118 &
1-24\tabularnewline
Williams et al. 2019 & Wildlife Society Bulletin & 43 &
590-598\tabularnewline
Wood et al. 2019 & Biological Conservation & 230 & 67-74\tabularnewline
Amira et al. 2018 & Wetlands Ecology and Management & 26 &
865-877\tabularnewline
Alvarez-Vazquez et al. 2018 & Science of the Total Environment & 637 &
803-812\tabularnewline
Aticho et al. 2018 & Global Ecology and Conservation & 16 &
e00459\tabularnewline
Barboza \& Jorde 2018 & Journal of Comparative Physiology B-Biochemical
Systems and Environmental Physiology & 188 & 831-842\tabularnewline
Bieri et al. 2018 & Natural Resource Modeling & 31 &
e12156\tabularnewline
Cheng et al. 2018 & Acta Ecologica Sinica & 38 & 485-492\tabularnewline
Colwell \& Feucht 2018 & Wader Study & 125 & 135-141\tabularnewline
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Duettmann et al. 2018 & Bird Study & 65 & 516-524\tabularnewline
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1877-1890\tabularnewline
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Jia et al. 2018 & Biological Conservation & 218 & 240-246\tabularnewline
Johns et al. 2018 & Functional Ecology & 32 & 345-357\tabularnewline
Karlikova et al. 2018 & Journal of Ornithology & 159 &
555-563\tabularnewline
Kentie et al. 2018 & Global Change Biology & 24 &
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Kuo \& Wang 2018 & Irrigation and Drainage & 67 & 130-139\tabularnewline
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Loonstra et al. 2018 & Ibis & 160 & 89-100\tabularnewline
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http://dx.doi.org/10.6073/pasta/d7416d193cc1166f4ba67adad3f740c3\tabularnewline
Smith 1995 & Wilson Bulletin & 107 & 437-451\tabularnewline
Batzer \& Resh 1994 & Global Wetlands: Old World and New. & NA &
825-832\tabularnewline
Hickman 1994 & Ecological Engineering & 3 & 485-494\tabularnewline
Jackson 1994 & Ibis & 136 & 463-473\tabularnewline
Mallory et al. 1994 & Journal of Wildlife Management & 58 &
552-560\tabularnewline
Merendino \& Ankney 1994 & Condor & 96 & 411-421\tabularnewline
Steadman \& Intoh 1994 & Pacific Science & 48 & 116-135\tabularnewline
Bradley \& Bradley 1993 & Condor & 95 & 372-376\tabularnewline
Batzer \& Resh 1992 & Journal of the American Mosquito Control
Association & 8 & 117-125\tabularnewline
Merendino et al. 1992 & Wildlife Society Bulletin & 20 &
171-175\tabularnewline
Kohler \& Rauer 1991 & Wader Study Group Bulletin & 61 &
82-85\tabularnewline
Kumar \& Krishanan 1991 & Records of the Zoological Survey of India & 89
& 71-87\tabularnewline
Kantrud 1990 & U S Forest Service General Technical Report Rm-194 & NA &
93-123\tabularnewline
Brush et al. 1986 & Colonial Waterbirds & 9 & 189-195\tabularnewline
Cohen 1986 & Environmental Quality and Ecosystem Stability. Volume 3/A
\& 3/B. Proc. of the 3rd Int. Conference of the Israel Soc. For Ecology
\& Environmental Quality Sciences, Jerusalem, June 1-4, 1986. & NA &
95-105\tabularnewline
Powell \& Powell 1986 & Biological Conservation & 36 &
101-113\tabularnewline
Hobaugh 1985 & Journal of Wildlife Management & 49 &
1028-1037\tabularnewline
Leitch \& Kaminski 1985 & Journal of Wildlife Management & 49 &
212-222\tabularnewline
Guthery \& Bryant 1982 & Wildlife Society Bulletin & 10 &
309-317\tabularnewline
\bottomrule
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