Discussion
In this study, we applied integrated modelling procedures (SEM) to
productivity and waterbirds functional diversity data collected from
lakes and reservoirs in the arid and semi-arid northern China covering
over 5 million km2 with the aim to explore
macroecological pattern involving geographic gradients in inland aquatic
systems. Both lake productivity and waterbirds FD displayed strong
geographical variations across northern China (Figure 3). We found that
the geographic position exerted effects on lake productivity and
waterbirds FD in a similar way, i.e. through its influences on climatic
conditions, which was defined by seven bioclimatic variables in this
study. This causality from geographic position to climatic conditions
was significant and consistent in all three SEMs. Specifically, our
analyses showed an unambiguous decreasing elevational gradient for both
lake productivity and waterbirds FD (Herzog, Kessler, & Bach, 2005;
Rahbek, 1995). An increasing gradient with location coordinates was also
obvious; and the effects of latitude (through its effects on climate) on
both lake productivity and waterbirds FD were relatively weaker than
those of longitude. Moreover, the “latitudinal gradient”, which
predicts species diversity decreases when moving away from the equator
towards northern latitudes (Jetz, Thomas, Joy, Hartmann, & Mooers,
2012), was not supported. Instead, we found evidence of a reverse trend
in our analyses (Figure 4a). The opposite latitudinal gradient in this
study should be treated with cautions as the study focused on the
temperate zone that have a rather narrow latitudinal range and strong
negative effects of elevation might obscure the real latitudinal
pattern. Nevertheless, our analysis results supported a number of
hypotheses underlying the geographic gradients by Field et al. (2009):
1) Climate; 2) Productivity; and 3) Size of ecosystem. More importantly,
our results revealed a hierarchical structure of drivers that regulate
the observed biodiversity and productivity patterns (Macneil et al.,
2009).
Drivers of geographic and elevational patterns of lake
productivity
The lake productivity model had relatively high performance in
explaining the observed geographic and elevational gradients
(R2 for
separated and integrated models was 0.93 and 0.96, respectively, Figure
5 and 7). Both linear regression analysis (Figure 4d, 4e) and structural
equation modeling (Figure 5 and 7) showed that lake productivity was
positively related with water TN and TP concentration. Moreover, the
results from the integrated SEM indicated that the effect of nutrient,
which was defined by TN and TP concentrations in water, was much
stronger than that of climate. The close relationship between nutrient,
lake chlorophyll a concentration, and lake productivity (Jeppesen et
al., 2005; Smith, 1979) was expected because nitrogen and phosphorus are
important limiting nutrients in freshwater ecosystems (Elser et al.,
2007; Schindler, 1974). Lake ecosystem productivity depends on the
supply of nutrients, especially phosphorus (Wetzel, 2001). According to
previous studies, phosphorus loading alone could explain 79-95% of the
variances in lake chlorophyll a concentration (Schindler, 1978).
Climatic conditions, measured mainly by temperature and precipitation
variables including mean temperature of the wettest quarter, mean
temperature of the warmest quarter, annual total precipitation,
precipitation of driest month, and precipitation of the warmest quarter
also had positive effects on lake productivity, except for mean diurnal
range which showed the opposite trend (Figure 5 and 7). Climatic
variation has been found to influence the magnitude of chlorophylla concentration (O’Reilly, Alin, Plisnier, Cohen, & Mckee,
2003). Lake productivity increased with air temperature, which is the
function of solar energy input. (Warner & Lesht, 2015) reported that
air temperature and precipitation were identified as important
predictors, which had positive effects on chlorophyll a . Our
results are consistent with those studies, although the mechanisms are
not clear. One possibility is that higher air temperature reduces ice
cover, which facilitates wind-induced mixing and nutrient resuspension
(Nicholls, 1998; Schwab, Eadie, Assel, & Roebber, 2009). And increases
in the form of rain would cause increased run-off, which could bring
more nutrients to the lakes from non-monitored or diffuse non-point
sources. However, these hypotheses were not fully explored in our study,
and further researches are needed to understand the mechanisms on how
climate influences lake productivity. In addition, in our study, it was
mean diurnal range, mean temperature of the wettest quarter, mean
temperature of the warmest quarter showed significant effects on lake
productivity. For which, it may due to the sampling time (i.e. we
monitored lake chl-α in summer).
Drivers of geographic and elevational patterns of waterbirds
functional
diversity
The waterbirds FD SEMs achieved sufficient performance in explaining the
spatial variations in waterbirds FD (R2 for
both separated and integrated models were 0.52 and 0.56 respectively,
Figure 6 and 7). Geographic position, defined by latitude, longitude and
altitude, had dominating effect on waterbirds FD through its influence
on climate. Climate is typically a strong descriptor of broad-scale
richness patterns (Hawkins et al., 2003), and the theory that climate’s
control of energy drives the global richness gradient has generated an
extensive literature quantifying the relationship between species
richness and climatic variables (Whittaker et al., 2007). Our results
demonstrated that waterbirds FD increased with temperature, giving
empirical affirmation to the species-energy hypothesis in that species
diversity increases with environmental temperature (Allen, Brown, &
Gillooly, 2002). Temperature is one of major determinants of latitudinal
and altitudinal gradients in animal diversity (Allen et al., 2002;
Rohde, 1992), which may be explained by energy hypothesis, although the
underlying mechanism remains unknown (Hawkins et al., 2003). The model
results also showed that waterbirds FD increased significantly with
precipitation. Furthermore, the modelled path coefficients indicated
that precipitation was more important than that of temperature in our
system. Precipitation is one of the resource-based estimates of
available energy, especially in arid and semi-arid ecosystems (Brown &
Davidson, 1977), where biodiversity patterns are strongly related to
precipitation amount (Waide et
al., 1999). This is particularly true for waterbirds, whose distribution
is generally determined by rainfall (Wen et al., 2016) through changing
habitat availability, like water depth, habitat area and habitat
diversity (Canepuccia, Isacch, Gagliardini, Escalante, & Iribarne,
2007). As most of the lakes included in our study are located in the
arid and semi-arid region of China, precipitation is critical to
maintain enough habitat area for waterbirds.
Lake morphology, which was defined solely by lake area in this study,
had positive effect on waterbirds FD. This pattern
resembles the common species-area
relationship observed in many ecosystems (Arrhenius, 1921; Guadagnin,
Maltchik, & Fonseca, 2009; Keil, Storch, & Jetz, 2015; MacArthur &
Wilson, 1967; Nogues-Bravo & Araujo, 2006; Rosenzweig, 1995). According
to the theory of island biogeography (MacArthur & Wilson, 1967), large
and more diverse ecosystems are likely to harbor more species due to
higher immigration rates and lower extinction rates. Indeed, lakes in
arid and semi-arid zones can be regarded as aquatic islands in a
terrestrial world, offering an explanation for the positive species-area
relation in our analysis. This finding is consistent with Suter (Suter,
1994), who reported species-area relationship for waterfowl assemblages
on the 20 major Swiss lakes north of the Alps. Likewise, Froneman et al.
(2001) described a positive species-area relationship in waterbirds
communities in farm ponds, South Africa, and Guadagnin et al. (2009)
also described that the number of waterbirds species in wetlands from
the Atlantic coastal zone of Brazil showed a positive relationship with
their size.
Relationship between waterbirds species richness and lake
productivity
Many studies revealed that productivity affects diversity (Carpenter et
al., 1987; Dodson et al., 2000; Mittelbach et al., 2001), especially for
plants (Chase & Leibold, 2002). Nonetheless, no general consensus
concerning the form of the pattern has emerged based on theoretical
considerations or empirical findings (Waide et al., 1999). And positive,
negative, and hump-shaped patterns were common at most spatial scales
and no one pattern predominated (Mittelbach et al., 2001). For avian
species, particularly waterbirds, there are only a few studies presented
the relationship between diversity and productivity (Hawkins et al.,
2003; Hurlbert & Allen, 2004). Results of our integrated SEM gave
evidence to support the causality from lake productivity to waterbirds
FD albeit the relationship was weak in comparison to other factors. As
the majority of waterbirds forage on the riparian zone of lakes (mean
trait value of foraging at ground was greater than 55% for all lakes,
Appendix A1) and have plants as their major diets (Appendix A2), this
weak causality is expected.
Conclusions and caveats
A major contribution of this study is that our findings reveal the key
environmental drivers of large-scale patterns in lake productivity and
functional diversity using advanced statistical techniques (i.e. SEM).
This approach showed that the observed geographical and altitudinal
gradients in lake productivity and waterbirds FD (Figure 3) can be
partly explicated by the gradients in climatic conditions, which is in
term significantly related to the geographic position of the lakes on
the earth surface. As the relationship between productivity and species
diversity in arid and semi-arid ecosystems has not been addressed, our
study could contribute to the mechanistic explanations underlying the
observed broad – scale biodiversity gradients in arid and semi-arid
region. However, site-specific factors, such as lake morphology (for
waterbirds) and nutrients (for productivity) impose their effects
independently (Figure 5-7), and their effects could be more important
than climatic variables (e.g. for lake productivity). These results,
although supporting some primary macroecological biodiversity theories
such as species–energy and species–resource hypothesis, could not lead
to a mechanism that unifies these theories (Mcgill, 2010), exemplifying
one key limitation of statistical analyses: statistical relationships do
not necessarily reveal the underlying mechanisms regulating waterbirds
biodiversity (Stomp et al., 2011). For example, the SEM indicates that
altitude has a strong negative effect (indirectly through climate and
lake productivity, Figure 7) on waterbirds FD. However, these causal
paths could be driven by other environmental variables that co-vary with
altitude but were not measured in our study. For instance, seasonal
variation in environmental conditions increases at higher elevation,
which could reduce species diversity by excluding sensitive species with
a narrow tolerance range (Currie et al., 2004). Nevertheless, through
articulating the dominant processes, our results could contribute to
future studies seeking mechanistic explanations underlying the observed
macroecological phenomena.