Wind dispersal results in a gradient of dispersal limitation and environmental match among discrete aquatic habitats
Submitted by editor on 20 October 2015. Get the paper!by Zsófia Horváth, Csaba F. Vad, Robert Ptacnik
The Seewinkel region lies on the bordering lowland area between eastern Austria and western Hungary. It has sodic soil and several extremely shallow (<1 m) aquatic habitats, so-called soda pans were formed in this confined region. Soda pans are saline temporary habitats drying out generally during late summer (Figure 1). The zooplankton communities reach enormous densities in these naturally hypertrophic habitats and represent an important resource for migratory birds.
Figure 1. Cluster of soda pans in the Austrian Seewinkel region (left; source: Google Earth). Such habitat networks are ideal study cases for metacommunity research. Right: soda pans in their wet and dry phase. Photos: Zsófia Horváth.
Due to the topography of the surrounding areas, the region experiences very constant northwestern winds. In fact, on a continental scale, this northwestern part of the Pannonian lowlands is situated in a “wind tunnel” between the eastern mountains of the Alps and the westernmost parts of the Carpathian Mountains. The area hence is also favourable for wind power and currently hosts the seventh largest onshore wind farm of Europe (The European Wind Energy Association, 2013; Figure 2).
Figure 2. Wind farm near the studied habitats. Photo: Zsófia Horváth.
Prior to our study, no research has revealed directionality in a metacommunity consisting of distinct habitats. We recognised that the Seewinkel region provided an ideal study case for this: the soda pans form a habitat network in which zooplankton most likely travels passively by wind in the form of resting stages.
We aimed to test explicitly for directional signals in the spatial pattern of zooplankton (Rotifera, Cladocera, Copepoda; Figure 3). For this, we used an innovative approach by applying a spatial statistical method (Asymmetric Eigenvector Maps, AEM) developed primarily for fluvial networks.
Figure 3. Examples of the three main zooplankter groups we investigated: Cladocera, Copepoda and Rotifera (left to right). Background shows the lakebed of a soda pan in summer, after drying out. Hhotos: Zsófia Horváth.
We were able to identify directionality corresponding to the main wind direction in the spatial structure of communities (Figure 4). Furthermore, the match between community composition and environmental conditions exhibited a spatial pattern consistent with the prevailing wind corridor, with best match found downwind the dominant wind direction. Our study therefore showed that dispersal limitation may constrain community assembly in highly mobile organisms even at spatial scales below 5 km.
Our results highlight that a proper representation of dispersal routes is mandatory for identifying the processes shaping metacommunities. It is very likely that many lentic systems, including pools, ponds and lakes, are affected by strong wind dispersal, especially those of temporary nature. Therefore, exploring the possible directional similarities in such habitat complexes with the help of asymmetric methods like AEM could significantly contribute to our understanding of spatial patterns in metacommunities.
Figure 4. Directionality in the zooplankton metacommunity. Radar charts represent the annual wind direction distribution in the study area in % (left) and the variation explained by the pure spatial effects in respective angles of the directional component (middle). Right: the mismatch between community data and local environment, projected for the region. Points illustrate the observed values, while the background shows the values predicted by our model. Here the highest mismatch values are found in the upwind part of the area, while mismatch decreases downwind, as connectivity by wind dispersal between the habitats increases.