Introduction
One of the oldest and most important macroecology patterns is the
spatial variability in biodiversity over broad geographical extents
(Field et al., 2009; Hawkins, 2001; Stein, Gerstner, & Kreft, 2014), an
appropriate knowledge of which is an essential key to guarantee the
provision of multiple ecosystem services (Benedetti et al., 2020). The
planet Earth shows striking gradients in the diversity of plants and
animals, from high biodiversity in the tropics to low biodiversity in
polar and high-mountain regions (Gaston, 2000; Rosenzweig, 1995;
Whittaker, Nogues-Bravo, & Araujo, 2007; Willig, Kaufman, & Stevens,
2003). Due to the alarming rate of biodiversity loss in the last decades
caused by anthropogenic interruption (Prescott et al., 2016; Waide et
al., 1999), a modern resurgence of interests in taxonomic diversity
patterns is likely to contribute important insights for developing a
more general theory of species
diversity (Castro‐Insua,
Gómez‐Rodríguez, & Baselga, 2016). Studies in the past decades has
documented the broad-scale spatial patterns of species diversity, and
explored the mechanisms for such patterns, leading to conceptual insight
on the biogeographical variation of species diversity (Devictor et al.,
2010; Field et al., 2009; Gaston, 2000). For example, diversity is often
highest at intermediate levels of ecosystem productivity (Grime, 1973;
Mittelbach et al., 2001; Waide et al., 1999), and species diversity
generally increases with habitat area (MacArthur & Wilson, 1967;
Rosenzweig, 1995). In addition, environmental heterogeneity is
considered as a universal driver of species diversity across taxa,
biomes and spatial scales (Stein et al., 2014). Importantly, both
theoretical considerations and empirical analyses suggest that patterns
are likely scale dependent (Field et al., 2009; Mittelbach et al., 2001;
Mouchet et al., 2015).
Most macroecology researches have been focused on terrestrial ecosystems
(Currie & Paquin, 1987; Qian, Ricklefs, & White, 2005), and relative
fewer studies have explored geographic biodiversity gradients and the
underling mechanisms for aquatic ecosystems (Astorga, Heino, Luoto, &
Muotka, 2011; Barbour & Brown, 1974; Heino, 2002, 2011; Irz, Argillier,
& Thierry, 2004; Jacobsen, 2004), especially the aquatic ecosystems in
the arid and semi-arid region, which provide important habitat for
diverse species and water resource for human living (Williams, 1999).
Moreover, the geographical distribution of study sites is strongly
biased towards Europe and North America, with particularly poor coverage
in Asia (Field et al., 2009; Fu, Wu, Wang, Lei, & Chen, 2004). Study of
species diversity in aquatic ecosystems is as essential as in their
terrestrial counterparts (Stendera et al., 2012). Declines in
biodiversity are far greater in freshwaters than in most terrestrial
ecosystems (Sala et al., 2000), and freshwater ecosystems may well be
the most endangered ecosystems in the world (Dudgeon et al., 2006;
Millennium Ecosystem Assessment, 2005). In addition, the actual rates of
freshwater species extinction due to human interruptions are much higher
than natural extinction rates (Naiman & Dudgeon, 2011). Therefore, a
better understanding of the global freshwater diversity gradients and
the major environmental drivers remains a major topic (Heino, 2011); and
such studies serve to address some fundamental questions for conserving
freshwater taxa (Tisseuil et al., 2013).
In addition, biodiversity assessments are an important component of
conservation planning and increasingly used to identify land-use
management practices that maximise both evolutionary value and ecosystem
function (Chapman, Tobias, Edwards, Davies, & Vamosi, 2018). Key
requirements are to maintain community resilience to environmental
disturbance and to preserve ecosystem functions and services across time
and space (Socolar, Gilroy, Kunin, & Edwards, 2016). Consequently, it
is often proposed that we need to look beyond merely conserving species
richness towards maintaining the maximum diversity of evolutionary
lineages and associated ecological functions (Bregman et al., 2016). The
idea that functional diversity or functional complementarity performs
better than species richness as predictors of ecosystem functions is
supported by a range of empirical studies (Flynn, Mirotchnick, Jain,
Palmer, & Naeem, 2011; Fründ, Dormann, Holzschuh, & Tscharntke, 2013;
Petchey & Gaston, 2007). Functional diversity (FD) is a biodiversity
component that represents the extent of the functional differences among
species based on the distinction of their morphological, physiological
and ecological traits (Petchey & Gaston, 2006). Species loss may lead
to a reduction in FD depending on the intrinsic redundancy of
assemblages (Flynn et al., 2009; Petchey, Evans, Fishburn, & Gaston,
2007). A decrease on the FD of local and regional assemblages could have
dramatic consequences for ecosystem functioning because the traits of
species, not just the number of taxonomic units, ultimately drive
biodiversity-ecosystem functioning relationships (Dı́az & Cabido, 2001;
Hooper et al., 2005). Worldwide, strong geographic differences exist in
the ecological attributes of birds (Kissling, Sekercioglu, & Jetz,
2012). Thus, we ask how these factors combined to affect the diversity
of species assemblages under different environmental conditions.
Waterbirds are ubiquitous components of freshwater systems, and their
diversity and abundance have long been recognized as suitable
bioindicators of environmental change in aquatic systems (Caro &
O’Doherty, 1999; Wen, Saintilan, Reid, & Colloff, 2016) and serves
multiple significant functional roles in ecosystems (Barbet-Massin &
Jetz, 2015) (Figure 1). However, as with other freshwater biota,
macroecological studies of environmental drivers of waterbirds diversity
are rare (Shah, Domisch, Pauls, Haase, & Jähnig, 2014; Stendera et al.,
2012; Zeng et al., 2019). It is unclear whether similar latitudinal and
other broad geographical (e.g. altitudinal) gradients apply to
waterbirds as well. In a recent review, Heino (2011) found no clear
latitudinal gradients at regional scale while species richness typically
attains highest levels in mountainous regions. Using river basins as the
spatial unit, however, Tisseuil et al. (2013) found that the
‘climate/productivity’ hypothesis (Field et al., 2009) explained large
portion of geographic variance in waterbirds richness, which is
consistent to land avian species (Storch et al., 2006). Several factors
are known to affect waterbirds diversity at a local scale, such as lake
productivity, lake size, and habitat heterogeneity (Barbour & Brown,
1974; Cintra, 2015; Xia et al., 2016). Linking these local scale
variables with broad-scale geographical variations in an integrative
analysis framework could potentially articulate the leading processes
underlying the regional and global waterbirds richness patterns.
Lakes are ideal systems for studying the relationship between species
richness and productivity (Dodson, Arnott, & Cottingham, 2000). It
provides a unique system to examine macro-ecological pattern involving
geographic and elevational gradients in inland aquatic systems. In this
study, we apply multiple regression analysis and structural equation
models (SEM) to lake productivity and waterbirds FD data collected from
35 lakes and reservoirs across the temperate zone of arid and semi-arid
China to examine macro-ecological pattern involving geographic and
elevational gradients in inland aquatic systems. Our primary goal is to
test the causal path of geographic location climate lake productivity
waterbirds FD. In particular, we first develop separate models for lake
productivity and waterbirds FD to compare the dominant factors
controlling the gradients of the two indicators. We then build an
integrative SEM with explicit causality from productivity to waterbirds
diversity to quantify the effect of lake productivity on waterbirds FD.
Lake productivity is often a strong predictor of freshwater biodiversity
(Dodson et al., 2000), including aquatic animals (and zooplankton as
well) (Chase & Leibold, 2002) and phytoplankton (Stomp, Huisman,
Mittelbach, Litchman, & Klausmeier, 2011). But its effect on waterbirds
has rarely been tested. The productivity-richness hypothesis suggests a
positive effect of primary productivity on species richness by allowing
larger populations to persist, thereby reducing extinction risk and
supporting a higher diversity of niche specialists (Tittensor et al.,
2010; Willig et al., 2003). In this context, a significantly positive
path coefficient from lake productivity to waterbirds could provide
strong support to the productivity-richness hypothesis. However, a weak
or no causal link between the two might suggest that waterbirds, not
like other aquatic biota like fish (Dodson, 2008) that is constrained by
water, have access to other energy sources; and their distribution is
governed by other broad scale factors. In this study, we applied FD
metrics to waterbird communities sampled in 35 lakes across the whole
temperate arid and semi-arid northern China to test the
productivity-richness hypothesis, and more importantly, to fill in the
gaps in our understanding of ecological patterns in aquatic ecosystems
across a large geographical scale.