Synchrony is a significant and recurring result for different taxonomic resolutions at the present time series study. The synchrony of zooplankton populations have been detected in different types of ecosystems, both temperate (Rusak et al. 1999, 2002) and tropical (Caliman et al. 2010). The results obtained in this study and for several other groups of organisms (from insects to mammals: Moran 1953a, b; Ranta et al 1995; Koenig and Knops 1998; Cattadori and Hudson 1999; Shanker and Sukumar 1999; Koenig 2001; Lansac-T??ha et al 2008; Stange et al 2011) and different spatial scales (Koenig and Knops 2013; Seebens et al 2013) strongly suggest that, like environmental synchrony (Koenig 2002) the population synchrony is an ubiquitous phenomenon.
Different processes can generally cause population synchrony (Moran 1953a; Paradis et al. 2000; Koenig 2002). One possible cause is the dispersion that can lead to synchronization in environments with populations spatially close enough to allow continuous exchange of individuals. Passive transport by water currents induced for wind can drive the horizontal distribution of lacustrine zooplankton (Seebens et al. 2013). In reservoirs, the flow of water from upstream stream ecosystems may be the main factor determining the horizontal distribution of zooplankton (Marzolf 1990). Hence, the transport phenomena can have great importance as synchronizing agent in reservoirs (Lansac-T??ha et al. 2008). For Protozoa, in part, this scenario seems plausible when the explanatory variables used were spatial and environmental distances. The importance of the spatial component can be explained considering this a pseudoplanktonic group. Thus, the dynamics of these organisms is associated with fluctuations in the water flow that transport individuals from the substrate into the water column (Lansac-T??ha et al 2008; Velho et al 2013).
The Moran effect (Moran 1953a) is another possible cause for the population synchrony, that is, driven by environmental factors synchrony. This study results indicate that the similarity of limnological dynamics (i.e. environmental synchrony matrix) and/or similarity between sites considering the average of the limnological variables (i.e. environmental distance matrix) were important determinants of population synchrony. On the other hand, most of population synchrony matrices showed no significant relationship with distance. In addition to this, the average level of environmental synchrony showed a decay with increasing distance. Therefore, this combination of results (i.e., the greater importance of environmental similarity between different regions of the reservoir than that of geographic distance), is a strong evidence that the spatial synchrony of zooplankton populations was strongly influenced by environmental synchrony. In this context, the inference about the importance of the Moran effect can be maintained even though environmental synchrony has declined with increasing geographic distance (Ranta et al 1999; Koenig 2002; Lansac-T??ha et al 2008; Fox et al 2011). This result is consistent with a growing number of studies that point the Moran effect as an important factor driving population synchrony (Hudson and Cattadori 1999; Lundberg et al 2000; Lima-Ribeiro et al 2007; Koenig and Knops 2013).
Different limnological variables (related to density dependent and independent processes) can influence demographic rates of zooplankton populations. Although it is difficult to establish the relative importance of different variables measured in determining patterns of synchrony, the results of the simple Mantel test suggest that the main variables (i.e., those with average correlation higher than 0.6) were nitrate, transparency, electrical conductivity, temperature and chlorophyll-a. To a greater or lesser degree, these variables are correlated with meteorological conditions. For example, precipitation may increase rates of runoff and the concentration of ions. Assuming this process, it is also expected the water transparency decrease followed by increase of nitrate levels. Variables strongly influenced by weather conditions were also major determinant in synchrony of aquatic organisms density reported in many studies (Magnuson et al 1990; Grenouillet et al 2001; Anneville et al 2004; Rusak et al 2008).
Some studies have shown that variables with more direct association to the biological component of the system have less predictive power for the spatial population synchrony (Kratz et al 1997; Baines et al 2000; Rusak et al 2008; Caliman et al 2010). However, the results of this study showed that the matrix of environmental synchrony based on chlorophyll-a were significantly correlated with synchrony matrices for protozoans, rotifers and copepods in Lajes reservoir. Taking into account that Lajes is an environment with low phytoplankton biomass (Soares et al. 2008), the dynamics of this variable can directly affect the dynamics of zooplankton.
While previous synchrony studies were developed in discrete systems (e.g., different lakes), a unique environment as such reservoir can also be considered an excellent system to understand ecological synchrony due to its spatial heterogeneity. The present work demonstrates the existence of spatial synchrony at zooplankton community and environmental variables. For this particular situation, the levels of synchronization within the monitoring area indicate that temporal variation patterns are similar regardless of the sampled site. However, the values of synchrony were not so high to justify a reduction in the number of monitoring points (Rhodes and Jonz??n 2011).
The importance of regionalized environmental dynamics in synchronizing the population dynamics has often been suggested (Haynes et al 2013), including multiple studies in aquatic ecosystems (Cottenie et al 2003; Kent et al 2007; Seebens et al 2013). However, surprisingly few studies, in particular with planktonic populations (e.g., George et al. 2000) demonstrated an association between population and environmental synchrony matrices. An auspicious direction for future work would be to share the data (used in previous studies on population synchrony) with the objective of quantifying the relative importance of the main synchronizing agents using a standardized analytical protocol (as the one used by Haynes et al. (2013), and in the present study). This is an open invitation to interested.
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