Landscape Genetics (M1, M3); Keller, van Strien, Ghazoul, Holderegger 27

Landscape genetics of in intensive agriculture: new ecological insights

Daniela Keller1, Maarten J. van Strien1, Jaboury Ghazoul2, Rolf Holderegger1

1 Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf 2 Institute of Terrestrial Ecosystems, Universitätsstrasse 16, ETH Zürich

Summary Agricultural landscapes harbour various species, of which many became threatened due to frag- mentation, habitat loss and agricultural intensification. Connectivity measures are being implemented to mitigate this trend. However, to determine whether structural connectivity measures are truly effective, the functional connectivity needs to be measured, which requires knowledge on species-specific migration rates as well as the identification of landscape elements enhancing or inhibiting migration.The latter is unknown for most insect species. Therefore, we studied the effects of landscape composition on migra- tion and gene flow of insect species inhabiting an intensively managed agricultural landscape in the Oberaargau region in Switzerland. We focussed on five study species inhabiting different habitat types: a wetland grasshopper ( grossum), three grasshoppers commonly found in ecological com- pensation areas and other habitat types (Chorthippus albomarginatus, Chorthippus biguttulus and Gom- phocerippus rufus), and a damselfly inhabiting ditches (Coenagrion mercuriale). For each of these spe- cies landscape elements that facilitated or inhibited migration and gene flow were assessed. Furthermore, we tested whether the reproductive habitat of a species was also the preferred migration habitat and whether this differed between short- and long-distance migration. Several landscape genetic approaches were applied to answer these issues. Transect analysis was used to identify landscape elements that enhance or inhibit gene flow within straight-line transects between pairs of populations. Because straight- line transects assume rectilinear migration, we developed a new method, least-cost transect analysis (LCTA), which creates transects around least-cost paths to give a better representation of the landscape that a migrating individual may encounter. LCTA was used to assess most likely migration habitats for short- and long-distance dispersal and simultaneously identify landscape effects on gene flow. For both, the damselfly C. mercuriale and the wetland grasshopper S. grossum, short-distance migration occurred predominantly within their reproductive habitat. For long-distance dispersal, however, C. mercuriale preferred open agricultural landscapes, and S. grossum did not show any landscape preferences. This differentiation between short and long-distance dispersal was further analysed making use of population network topologies. With network topologies we discovered that the spatial configuration of populations may have influence on the detectability of a landscape effect on migration and should be considered in landscape genetic analyses. A simulation study is currently being set up do determine to what extent results from landscape genetic techniques are influenced by habitat fragmentation and abundance.

Introduction About one third of the earth surface is currently used for agricultural purposes (FAO 2003). Through intensification and development, the survivability of many species inhabiting agricultural land is threat- ened. In Switzerland, many measures are being taken to enhance the ecological quality of intensive agricultural lands in order to increase biodiversity. For instance, the Swiss federal government has established an agri-environmental scheme, of which a primary goal is to connect isolated habitats with one another, if necessary by promoting the creation of new habitats to increase biodiversity (BAFU 2009). One of the measures taken under this scheme was the establishment of an Ecological Compensation Area (ECA) legislation in 1993. This legislation obliges Swiss farmers to extensively manage 7 % of their land in return for direct payments (Herzog et al. 2003). Additional measures that are taken to increase the attractiveness of some agricultural areas for hygrophilic species is the restoration of wetland areas and ditch systems (Fig. 1; Boschi et al. 2003). To make these areas truly effective, they should be functionally 28 ENHANCE

connected to other similar areas, in order to facilitate migration and gene flow between them (Tischendorf and Fahrig 2000a, b). To measure functional connectivity, the rate of migration between populations needs to be quantified, which can be done by mark-recapture studies (Ockinger and Smith 2008) or by using genetic (dis)similarity between populations as a measure of gene flow (Holderegger and agnerW 2008). Because the former usually underestimated migration rates (Van Dyck and Baguette 2005; Kamm et al. 2009), we preferred to utilize the latter. Genetic (dis)similarities are used to detect those landscape elements that enhance or inhibit gene flow. Such analyses are performed and studied in the novel disci- pline of landscape genetics (Manel et al. 2003).

This study was focussed on increasing our understanding of how landscape composition and configura- tion affects migration and gene flow between insect habitats in intensive agricultural land. We focussed on the following insect habitats: ecological compensation areas, ditch systems and wetland areas. For each of these habitats, we selected one or several focal species. Although the breeding and foraging habitat of each of the study species is fairly well known, we do not know much about their preferred migration habitats. Determining preferred migration habitat will be an essential first step in understanding how well habitats are functionally connected. By uncovering which landscape elements facilitate or inhibit gene flow, we can determine which populations are likely to be genetically isolated. For many insect orders, including the two orders of our focal species (i.e. and Odonata), it has been hypothesised that a population can consist of sedentary and migratory individuals (Dobzhansky and Wright 1943; Ibrahim et al. 1996; Stettmer 1996; Ward and Mill 2007). Therefore, it is important to consider that short-distance migration through dispersal of mainly sedentary individuals may be driven by other landscape characteris- tics than long-distance travel by migrating individuals.

Research questions Making use of several insect datasets and landscape genetic techniques, we addressed several research questions. First, we wanted to determine which landscape elements inhibit or facilitate gene flow between habitat patches in intensive agricultural land. This will help us to understand if gene flow between habitats is mainly directed through or along similar landscape elements or whether each habitat needs to be functionally connected by distinct landscape elements. Second, we wanted to asses whether the breeding and foraging habitat of our study species also was their preferred migration habitat. Third, we wanted to assess whether long- and short-distance migration happens through different migration habitats.

Fig. 1. Extensively managed ditch in the Oberaargau region. Photo: D. Keller. Landscape Genetics (M1, M3); Keller, van Strien, Ghazoul, Holderegger 29

To answer the above questions, we have applied and developed several landscape genetic techniques. To determine the most likely migration habitat, we have developed least-cost transect analysis. This method is also suitable to determine which landscape elements located along the most likely migration route are inhibitors or facilitators of gene flow. Furthermore, we built population networks to better understand the effect of spatial configuration of populations (or habitats) on landscape genetic results. We also set up a simulation study to determine the effects of migration behaviour combined with the level of fragmentation of habitat patches on the outcome of landscape genetic analyses.

Material and methods Study area The study area was situated in the North of the Canton of Bern, in an area known as Oberaargau. The main towns in the area are Langenthal and Herzogenbuchsee. The river Aare flows through the area in a West to East direction. The landscape is dominated by intensive agricultural land interwoven with patches of forest and smaller settlements (Fig. 2). There are varying densities of ECAs throughout the area. Previous studies have shown that some grasshopper species occur in higher densities in ECAs, while others occur in equally high densities on intensive agricultural land and ECAs (Albrecht et al. 2010). Furthermore, a large part of our study belongs to the Smaragd-area Oberaargau, which is one of the areas in the Swiss-wide species protection project Smaragd (www.smaragdoberaargau.ch). In this area, habitat maintenance and connectivity measures are being implemented for various endangered focal species. For instance, through special mowing regimes agricultural drainage ditches and small streams are upgraded for the endangered damselfly Coenagrion mercuriale (www.smaragdoberaargau.ch/index. php?option=com_content&view=article&id=104&Itemid=99). The valley basins in our study area used to be characterized by seasonally flooded hay meadows (German: Wässermatten).This practice was aban- doned at the beginning of the 20th century, but recently hay meadows are being flooded again to restore their former unique flora and fauna (Boschi et al. 2003). A hygrophilic species that thrives particularly well on these floodplains is the large marsh grasshopper Stethophyma grossum.

Fig. 2. Typical landscape in the Oberaargau study area. Intensive agricultural land is interspersed with forest patches and residential areas. In the background one can see the Jura mountains. Photo: M.J. van Strien. 30 ENHANCE

Datasets We chose the Southern Damselfly Coenagrion mercuriale as focal species for ditch habitats. The breed- ing habitat of this species in the Oberaargau are slow-flowing, calcareous, summer-warm streams and ditches (Sternberg et al. 1999). However, it is unknown whether the ditches currently inhabited by C. mer- curiale are functionally connected. A previous study (Hepenstrick 2008) has located and characterised all existing populations in the study area. Thus, a full sampling was possible for this species. We performed both a mark-release-resight study and a genetic analysis. In May and June 2009 we marked 455 individu- als at three different ditches (Fig. 3). We then determined longevity, resight-rate and maximal movement distance. After the mating season in July 2009, we also sampled 450 mid-legs of C. mercuriale individuals from 19 stream sections (Fig. 4) for genetic analysis with microsatellites (Watts et al. 2004).

The larger marsh grasshopper Stethophyma grossum can be found in wetland areas throughout the Oberaargau (Fig. 5). Development of eggs and larvae depends on high moisture content of the top soil (Marzelli 1995; Koschuh 2004). The breeding habitat of this species is, therefore, characterized by the availability of moist areas. In August and September 2010, we checked all streams, ditches, valley bot- toms and other moist areas in the Oberaargau for populations of S. grossum (Fig. 4). We sampled 963 individuals from 53 locations. At each location we took tissue samples from up to 30 individuals. These individuals were genotyped with microsatellites that we developed for this study making use of next gen- eration sequencing techniques (Keller et al. in press).

Fig. 3. Two male Coenagrion mercuriale. Markings were applied to wings in order to recognise individuals in the mark-release-resight study. Photo: D. Keller. Landscape Genetics (M1, M3); Keller, van Strien, Ghazoul, Holderegger 31

Fig. 4. Overview of sampling designs of several landscape genetic studies investigating insect dispersal in the Oberaargau in 2009 and 2010.

Previous studies in Switzerland have observed an increase of Orthopteran species on newly established ECAs, but less so on surrounding intensive agricultural land (Schneider and Walter 2001). Therefore, we chose several grassland grasshopper species to determine if their reproductive habitat (i.e. ECAs) was also their main migration habitat. Albrecht et al. (2010) classified grasshopper species based on their affinity to ECAs. Stenotopic species predominantly occurred in ECAs, disperser species occurred in high densities in ECAs and their abundance decreases with distance to the ECA, and ubiquist species occurred in equal quantities in and outside ECAs. We selected two disperser (Chorthippus biguttulus and Gomphocerippus rufus) and one ubiquist (Chorthippus albomarginatus) as study species. No a priori population location information was available for these species in our study area. Therefore, we randomly selected 200 sample points of which half were located in ECAs and half on intensive agricultural land (Fig. 4). We checked each location for the presence of these species and sampled up to ten individuals per species and location. Since no microsatellite markers were available for the three study species, genetic analysis was performed using amplified fragment length polymorphisms (AFLP).

Landscape genetic methods Genetic clustering analysis combined with kriging interpolation and the overlay technique Bayesian genetic clustering methods group individuals into clusters based on genetic data. Some ­methods exclude (e.g. STRUCTURE; Pritchard et al. 2000) and others include spatial information (TESS; Chen et al. 2007).

For each individual, the assignment probability to each cluster is calculated, which can afterwards be interpolated over the entire study area using kriging. The resulting interpolated grids can then be used to detect landscape barriers to gene flow by overlaying land cover maps (i.e. the overlay approach; Storfer et al. 2010). 32 ENHANCE

Fig. 5. Mating pair of the larger marsh grasshopper Stethophyma grossum in the Oberaargau region. Photo: D. Keller.

Corridor/transect analysis Corridor (transect) analysis assesses landscape elements in straight-line corridors of a certain width between all pairs of populations (e.g. Emaresi et al. 2011). By regressing a distance matrix composed of genetic differentiation (pairwise FST) against distance matrices of landscape elements using multiple regression analysis on distance matrices (Lichstein 2007), landscape elements that enhance or hinder gene flow can be identified.

Least cost transect analysis (LCTA) To quantify the landscape between two populations, landscape geneticists often use resistance-to-move- ment surfaces (Adriaensen et al. 2003). However, parameterization of resistance surfaces is usually a subjective process. In contrast, transect-based approaches, as used above, might oversimplify dispersal patterns by assuming rectilinear migration between populations. In order to overcome these shortcom- ings, we developed an approach that combines these two techniques: least‐cost transect analysis (LCTA). In a first step, binary resistance surfaces representing migration habitat and matrix are created. Each selected landscape element is in turn regarded as preferred migration habitat, regardless of its hypothesized inhibitive or facilitative nature. In a second step, least-cost paths are calculated between all population pairs from these resistance surfaces. This path is subsequently buffered to create a transect of a certain width. The effective distance and the proportion of each landscape element is calculated in the transect to form a set of landscape predictor variables per resistance surface. As in the above corridor/ transect approach, we then use regression analyses to determine the most likely migration habitat and those landscape elements in the transects that enhance or inhibit gene flow.

Simulation study In the simulation program, populations were randomly placed in a habitat-matrix landscape. The propor- tion of habitat and the level of habitat fragmentation can be varied for each landscape. Probability of mi- gration between populations is derived from a migration distribution (Ibrahim et al. 1996). This distribution can be the result of a random walk (i.e. a normal distribution) or the result of many sedentary individuals and several long-distance migrators per population (i.e. a leptokurtic distribution). Each generation, in- dividuals either remain in their source population or migrate to other populations. Over a certain number of generations the genetic differentiation between populations is assessed. We can then determine if the input settings can be retrieved from a landscape genetic analysis of the data. Landscape Genetics (M1, M3); Keller, van Strien, Ghazoul, Holderegger 33

Network topology Network analysis has recently been introduced for assessing connectivity in ecology and conservation biology (Urban et al. 2009; Urban and Keitt, 2001). A network consists of a set of nodes, which are con- nected by edges. In ecology, habitats or populations can be defined as nodes and potential migration represents edges (Galpern et al. 2011). Edges are usually represented by geographic distances and often, nodes are only connected if their intermediate distance is below a certain distance threshold, which is the maximum migration distance of a species. Network topology can identify well-connected communi- ties of populations as well as “weak”, i.e. hardly connecting edges. By creating weighted networks, where edges are given weights according to genetic distances, for instance pairwise genetic differentiation (FST), connectivity can further be analysed.

Results and conclusions Species-specific results For the damselfly C. mercuriale, we found agriculture to be the main migration habitat. However, a detailed analysis separating different spatial scales of migration showed that frequently occurring short- distance migration (< 3 km) followed streams and ditches, i.e. the reproductive habitat, and that rare long- distance migration also crossed agricultural land. Elevation change (e.g. hill ridges), Euclidian distance and forest hindered gene flow and migration, while open agricultural land enhanced gene flow. Occur- rences of C. mercuriale in our study area form connected networks of populations if they are within the same ditch system and if there are no major forests or elevation obstacles separating the occurrences.

For the wetland grasshopper S. grossum, we also found the reproductive habitat to be the optimal migra- tion habitat for short-distance migration. For long-distance migration, however, no distinct preferred migra- tion habitat could be identified. Furthermore, we found that network topology could be used to define the spatial scale threshold up to which landscape elements affect gene flow. For short-distance migration, the proportion of water bodies and roads facilitated gene flow, whereas forests, settlements and path length acted as barriers. Population structure of this wetland species mainly coincided with the flood plains in the valley bottoms.

For the three grassland grasshoppers, a first analysis did not find any fectef of ECAs on their distribution and genetic patterns, which might be a result of high connectivity and massive gene flow in the studied landscape of the Oberaargau. A series of much more detailed analyses will be necessary to prove that this result really holds true (STRUCTURE, partial Mantel tests based on habitat suitability models and species-specific LCTA analyses). However, note that the above result means that for three common grasshoppers the agricultural landscape of the Swiss lowlands still provides connected habitat: a positive message.

Methodological results Least-cost transect analysis (LCTA) proved to be a useful approach to determine the most probable migration habitat of focal species. The method outperforms existing methods in a statistical way and produces results that are easier to interprete than those of traditional landscape genetic methods.

The simulation study is still in development. Preliminary results show that genetic patterns are mainly the result of stochastic processes and the level of fragmentation and type of migration are of much less importance. If this result is substantiated in further analyses, it would imply that that regional differences between genetic patterns may not be the result of differences in landscape composition and configuration, but mainly be the result of random gene flow events.

Mark-recapture studies proved to be useful for the detection of frequently occurring short-distance migra- tion, but not for rare long-distance migration events. However, these rare events are of particular impor- 34 ENHANCE

tance for maintaining connectivity in highly fragmented landscapes and can be characterised by using genetic methods.

Genetic measures are hence suitable to measure gene flow, especially over large distances. Our results also indicated that measures reflecting recent gene flow (e.g. from assignment tests) might be more ap- propriate for landscape genetic studies, especially if contemporary landscape configurations is used.

Population network topology can be used in landscape genetic analyses to assess the relevant spatial scale up to which landscape should affect gene flow. This requires, however, an almost complete sam- pling of all populations in a study area. In addition, mapping the reproductive habitat of a study species based on information from the literature can be used as a simple and effective alternative to habitat suit- ability analysis.

General conclusions Our studies suggest to differentiate between the reproductive and migration habitat of species in future landscape genetic studies.

We also showed that short- and long-distance migration may be directed through different migration habitats. For two study species, we found that only short-distance migration used the reproductive habitat as preferred migration habitat.

The threshold distance separating short- and long-distance migration can be explained by the spatial configuration of populations, i. e. with network topology.

The newly developed method of least-cost transect analysis LCTA provided a powerful landscape genetic tool to analyse migration habitats and detect landscape effects on migration and gene flow, which is important knowledge for the conservation management of connectivity.

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