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Conservation Genetics

, Volume 20, Issue 1, pp 7–17 | Cite as

Mix and match: regional admixture provenancing strikes a balance among different seed-sourcing strategies for ecological restoration

  • Anna BucharovaEmail author
  • Oliver Bossdorf
  • Norbert Hölzel
  • Johannes Kollmann
  • Rüdiger Prasse
  • Walter Durka
Perspective

Abstract

One of the main questions in ecosystem restoration is where to obtain the seeds to re-establish plant communities. While the most commonly advocated approach is to use seeds from local sources, some experts argue against this because local populations may harbour little genetic variability for the restored populations to be able to adapt to and survive global change. Instead, they propose alternative strategies such as mixing seeds from various sources to increase genetic variability and adaptive potential, or using seeds from populations that have a similar climate as predicted for the target locality in the future. All these alternative seed-sourcing strategies have in common that they involve a transplanting of plant ecotypes, sometimes over large spatial scales. This is risky because plants from distant origins may be maladapted to the current local abiotic and biotic environment. In addition, introduction of non-local provenances will disrupt natural patterns of within-species biodiversity and will affect ecological networks, with unpredictable consequences. To balance the value of local adaptation with the need for future adaptation potential, we propose ‘regional admixture provenancing’ as a compromise strategy. Here seeds are sourced from multiple populations within the same region as the target locality and mixed prior to use. The mixing of seeds will increase the genetic diversity necessary for future adaptation, while restricting seed origins to a regional scale will maintain regional adaptation and reduce the risk of unintended effects on other biota. This approach is feasible in practice and has recently been implemented in Germany. We believe that it represents a compromise to reconcile opposing views on ecological restoration.

Keywords

Local adaptation Regional adaptation Region of origin Seed mixture Seed sourcing strategy Seed transfer zone 

Introduction

Ecologists and environmental managers often wish to restore the biodiversity and ecological integrity of degraded ecosystems (SER 2004). In many cases, the first critical step in such ecological restoration is to re-introduce target plant communities that will successfully establish, persist for a long time, and provide essential ecosystem functions (Kettenring et al. 2014). Under favourable conditions, restoration can rely on natural succession as a ‘passive restoration’ (Prach et al. 2015a, b; Prach and del Moral 2015; Gilhaus et al. 2015), but in most cases it is necessary to introduce seeds from other sources to ensure restoration success (Hölzel et al. 2012). There has been an intensive debate on which specific seed sources to use for this (e.g. Broadhurst et al. 2008; Sgrò et al. 2011; Breed et al. 2013; Bucharova 2017).

The selection of appropriate seed source for restoration is affected by how large and how urgent the restoration project is. In intentionally planned projects, it is feasible to select and produce optimal seed. This type of restoration is the main focus of this article. We acknowledge that cases may exist, e.g. after large-scale disasters, where it is impossible to optimally source seeds. There, any native seed will serve the main purpose, that is revegetation in order to prevent spread of invasive species and further land degradation.

Current seed-sourcing strategies

Cultivars

A common seed source for restoration are commercial cultivars (e.g. Aubry et al. 2005). Cultivars are plant materials that are bred for specific target traits and propagated for large-scale use in fodder production or restoration. The seeds of cultivars are readily available in large amounts, which makes them an easy choice when large amounts of seeds are needed. Although seed availability is a key prerequisite of any restoration project, use of cultivars can be problematic, because the traits for which cultivars were bred differ from the traits that are favoured in the field, in restored populations (Leger and Baughman 2015). For example, grass cultivars have been bred for forage quality and yield, high seed production, seedling vigour, or drought tolerance. Although these traits seem intuitively advantageous for successful restoration, natural selection in the field favours different traits including early flowering, small plant size and higher root allocation (Leger and Baughman 2015).

The genetic background of cultivars is variable: some are clonal or highly inbred, while others are derived from polycrosses of multiple parents (Jones 2003; Kettenring et al. 2014). Cultivars with a narrow genetic basis will constitute genetically uniform populations that support less diverse ecological networks and fewer ecosystem functions (Barbour et al. 2016; Harvey et al. 2017). Many cultivars have low phenotypic variability, e.g. with respect to flowering phenology or secondary chemistry, and thus support a limited number of dependent organisms, which results in homogenization of communities across trophic levels (Zytynska and Preziosi 2011; Tahmasebi et al. 2014; Mody et al. 2017). Part of these problems can be counteracted by the use of polycrosses, but these genetically more diverse cultivars remain problematic, because they still constitute an introduction of foreign genotypes with risks of maladaptation and unpredictable effects on dependent organisms. Because of these problems, the use of cultivars should ideally be restricted to extreme habitats, such as heavily polluted sites, where wild plants cannot establish, or for emergency cases when large ecosystems must be quickly restored to prevent land degradation (Breed et al. 2018).

Local provenancing

The most common alternative to using cultivars in ecological restoration is the use of seeds of native plants that originate from wild populations, preferentially from local genotypes (Kiehl et al. 2014), i.e. local provenancing. This strategy is based on the observation that almost all plants show genetic and phenotypic differentiation among populations (Linhart and Grant 1996; Bossdorf et al. 2005; Jay et al. 2012), which often reflects adaptation to local environmental conditions such as soil, climate or biotic interactions (e.g. Keller et al. 1999). As a result, local plants on average perform better and have a higher fitness than plants of foreign origin (Oduor et al. 2016). This does not necessarily mean that a local provenance will always perform better than any particular foreign one at a given site. Provenance trials in forestry have shown that at individual planting sites, foreign provenances may grow better than the local ones (e.g. Gray et al. 2011; Gellie et al. 2016). However, when searching for a general, average pattern across all provenances, planting sites and/or across many species, local adaptation is the general pattern in woody and herbaceous plants (Oduor et al. 2016; Bucharova et al. 2017a). Moreover, many provenance trials of trees use growth in the first few years as a proxy of overall performance (e.g. Gray et al. 2011; McLane and Aitken 2012; Schreiber et al. 2013; Lu et al. 2014). Such growth-oriented measures of fitness can be problematic because populations likely possess conservative legacy of adaptation to extreme events, which may not be recognizable as intrinsically advantageous within few decades of early tree life if extreme events do not occur during that time (Whittet et al. 2016).

Despite the general evidence for local adaptation, seeds from a local population may also not be ideal for restoration in some cases. Local plants are adapted to past environmental conditions and may lack a sufficient amount of genetic variability to adapt to rapidly changing climatic conditions (Lesica and Allendorf 1999; Sgrò et al. 2011). Additionally, in modern landscapes with fragmented habitats, local populations might sometimes be small, genetically impoverished and/or inbred (Leimu et al. 2006). Consequently, it has been argued that seeds from such populations may be of low quality, and that the resulting offspring may fail to establish and to persist (Breed et al. 2013).

Matching future climate

A number of alternative seed-sourcing strategies has been suggested, which put greater emphasis on preadaptation to climate change and/or an enhanced genetic diversity (Fig. 1). Predictive provenancing (Crowe and Parker 2008; Sgrò et al. 2011) recommends the use of genotypes from areas that have similar climates as the one predicted for the target locality, whereas climate-adjusted provenancing (Prober et al. 2015) suggests introduction of several non-local ecotypes sourced along a climatic gradient to include higher genetic diversity and introduces genotypes adapted to future climate. The main aim of these strategies is to maximize population performance in the future under global change.

Fig. 1

The position of different seed-sourcing strategies within the inevitable trade-off between increasing local adaptation versus providing genetic variability. The different size of the seed-source populations reflects their relative contributions; the colour of the background represents an environmental gradient; modified after Prober et al. (2015) and McDonald et al. (2016). (Color figure online)

While these alternative seed-sourcing approaches are intuitive, the available evidence they are based on should be carefully considered. Some studies in fully controlled environments like growth chambers suggest that plants from drier and warmer origins are better adapted to heat and drought than plants from colder and wetter regions (e.g. Fernández et al. 1999; Matías et al. 2014; Dounavi et al. 2016; Varone et al. 2016; Taïbi et al. 2017). Experiments that are more realistic used a space-for-time approach, i.e. they simulated climate change by transplanting plants to warmer areas. This approach is also used in provenance trials in forestry, and it partly supports the results from controlled environments (e.g. Etterson 2004; Lu et al. 2014; Taïbi et al. 2016). Although the space-for-time approach is more realistic than growth-chamber experiments, its results must still be interpreted with caution since it exaggerates effect sizes compared to real climatic change (Elmendorf et al. 2015).

Both controlled-environment experiments and the space-for-time approach assume that climate is the main driver of plant performance. However, there are many other environmental factors besides climate that affect plant performance, e.g. soil conditions, photoperiod, neighbouring plants, herbivores or pathogens (Laine 2005; Leimu et al. 2012; Kalske et al. 2012; Alexander et al. 2015). In controlled environments these factors are excluded, but in space-for-time experiments the plants are generally planted into novel communities which also differ in soil conditions and harbour different interacting species. Thus, the space-for-time approach confounds climate change with other environmental changes and does not allow robust causal inference. The mismatch between home and target ecological communities can be of crucial importance (Araújo and Luoto 2007). For example, Alexander et al. (2015) showed that survival under climate change depends on the identity of interacting communities. This context-dependence of experimental outcomes together with the uncertainty of future community changes questions the outcome of simple space-for-time experiments. Although the communities of associated organisms are also likely to change under climate change, the resulting community will be always derived from the ones that are currently present (Elmendorf et al. 2015). Thus, we need in situ experiments that compare non-local, climate-adapted plants with the local plants under climate change conditions in a transplant experiment to the target locality.

We thoroughly searched the literature for such in situ experiments. A Web of Knowledge search (3rd July 2017) using “provenance climate change plant” as keywords resulted in 248 studies. From these, we selected experiments that (i) were carried out in common gardens or field sites, (ii) compared the performance of several provenances including a clearly defined local or regional one, and (iii) involved some kind of in situ climate change—through experimental manipulation or a natural climatic event like an extremely dry or hot year. We found only eleven studies that met these criteria, and they had inconsistent results. Two studies supported predictive provenancing (Schreiber et al. 2013; Wilczek et al. 2014), and in three studies the evidence was ambivalent because responses differed for different fitness traits (Thiel et al. 2014; Montwé et al. 2016) or among different climate-adapted provenances (Wellstein and Cianfaglione 2014). The remaining six studies provided evidence against predictive provenancing. In manipulated warmer climates or during natural climate events, provenances from drier or warmer origin performed equally or worse in comparison with the local provenances in forest trees (Taeger et al. 2013, 2015; Hancock and Hughes 2014; Baudis et al. 2014) and several herbs (Beierkuhnlein et al. 2011; Hancock and Hughes 2014; Bucharova et al. 2016a; Bjorkman et al. 2017). In summary, the available in situ studies so far provide very limited evidence that transplanting of climate-adapted ecotypes will lead to the desired effect of increased plant performance under climate change, probably because plants are adapted also to other environmental conditions and climate adaptation is not enough to predict plant performance (Bjorkman et al. 2017). Any decision on the use of climate-adapted provenances will require further experiments.

Maximizing adaptive potential

There are other seed-sourcing strategies, composite provenancing and admixture provenancing (Broadhurst et al. 2008; Breed et al. 2013), that do not focus on climate matching but on increasing overall genetic diversity with the idea that a large genetic variability will allow for selection of the best suited genotypes. Composite provenancing keeps the benefits and reduces the risks associated with seed transfer by combining the local sourcing approach with materials from more distant sources to mimic natural gene flow, while admixture provenancing maximizes the adaptive potential (and heterosis) with little regard for natural gene flow. Both strategies include transfer of plant genotypes, sometimes over considerable spatial scales.

Translocations over large geographic distances entail potential risks because geographic distance frequently correlates with both neutral and adaptive genetic differentiation in plants (de Kort et al. 2013; Sexton et al. 2014). This differentiation is partly because of isolation-by-distance effects, i.e. gene flow is becoming less likely with increasing geographic distance, and partly because geographic distance often correlates with environmental differences and thus is a proxy of adaptive differentiation (De Kort et al. 2013). Indeed, the strength of local adaptation often increases with geographical distance or correlated environmental dissimilarity, for example soil, altitude or climate (Becker et al. 2006; Ellis et al. 2007; Raabová et al. 2007; Volis et al. 2015; Hamann et al. 2016; Bucharova et al. 2017b). Consequently, gene flow between local and distant non-local native populations, as proposed in several seed-provenancing strategies, might result in “dilution” of local adaptation (Hufford and Mazer 2003; McKay et al. 2005; Crémieux et al. 2010). Furthermore, hybridization between genetically dissimilar individuals can cause outbreeding depression (Edmands 2007), especially if there are ploidy or major chromosomal polymorphisms between the provenances (Frankham et al. 2011), as is often the case in plants from several families commonly used for restoration (Kramer et al. 2018).

Probably the most important objection against the transfer of ecotypes over large spatial scale are the possible effects on interacting biota (Bucharova 2017). Since plants are the main primary producers in terrestrial ecosystems, a myriad of other organisms depends on them. These organisms frequently differentiate between plant genotypes, so that their performance will be influenced by plant origin (Leger and Forister 2005; Hull-Sanders et al. 2007; Abdala-Roberts and Marquis 2007; Herrera et al. 2011; Bischoff and Trémulot 2011; Laukkanen et al. 2012; Sinclair et al. 2015; Bucharova et al. 2016b). The current relationships between host plants and their interacting organisms are shaped by coevolutionary processes (Toju and Sota 2006; Garrido et al. 2012; Leimu et al. 2012), and introduction of foreign provenances will likely disrupt these relationships, with unpredictable consequences. However, there is a lack of long-term experiments that investigated the effects of translocations on biotic interactions.

Apart from microevolutionary processes such as selection and adaptation, intraspecific variation is also strongly influenced by historical processes, mainly Pleistocene climatic changes. This phenomenon is best known from the northern hemisphere, where glaciations and subsequent colonization of flora and fauna caused distinct patterns of within-species genetic differentiation, i.e. phylogeographic structure (Taberlet et al. 1998). However, Pleistocene glaciations had major imprints also on biota in the southern hemisphere. For example, Australia experienced extreme drought during Pleistocene glacial cycles and parts of the continents were covered by sand dunes. After retraction of the dunes in the interglacial, unsuitable habitats became suitable and were recolonized, resulting in phylogeographic patterns similar to those from the northern hemisphere (Byrne et al. 2008). Pleistocene climatic change also affected genetic structure of plant populations in Africa (Kebede et al. 2007; Kadu et al. 2013) and South America (Turchetto-Zolet et al. 2013). Together, these historical genetic patterns are an important part of global biodiversity and have intrinsic value (Ghilarov 2000). So far, the discussion on seed sourcing strategies did not consider preserving within-species patterns for their intrinsic value, mainly because the focus was on ecosystem services and productivity. However, we believe that within-species diversity is worth preserving similar to species diversity, at least in absence of evidence for conflict between biodiversity protection and maintenance of ecosystem functions. Translocation of seed material that ignores historical gene-flow corridors and barriers will disrupt these patterns.

In summary, the provenancing strategies discussed so far face a dilemma: (1) they either favour natural patterns of genetic structure and local adaptation at the expense of low genetic variability and inbreeding depression, or (2) they increase genetic variability and evolutionary potential but break up natural genetic patterns, risk outbreeding depression and maladaptation. Given increasing habitat fragmentation and global warming, a new approach that reconciles the two contrasting strategies for ecological restoration is needed.

Regional admixture provenancing

Here we describe a seed-sourcing strategy called regional admixture provenancing, which we believe represents a compromise between the existing approaches described above. In contrast to local provenancing, the seeds are collected from several large populations and mixed to provide high genetic variability (Havens et al. 2015; Espeland et al. 2017). In contrast to composite provenancing, simple admixture or climate-adjusted provenancing, the source populations should be located in a defined area (‘region’) around the target site with similar environmental conditions and a common biogeographical history to preserve regional adaptation and historical intraspecific patterns (Fig. 1).

The idea of sourcing seeds from a region is not new, and it is established practice, for example, in forestry. The regions for such seed transfers are called ‘seed transfer zones’. They are usually regions that share similar environmental characteristics and within which the species (seeds, seedlings, vegetative propagules or adults) can be transferred with no detrimental effects on mean population fitness (Hufford and Mazer 2003). So far, seed sourcing from seed transfer zones has been perceived as somewhat relaxed local provenancing, but did not require mixing of seeds from multiple origins to increase genetic variability (Vergeer et al. 2004; Williams et al. 2014; McDonald et al. 2016; but see Havens et al. 2015). We believe that a clear distinction between regional admixture provenancing and (relaxed) local seed provenancing is important, because the regional admixture provenancing as defined in this paper includes the mixing of seeds from several populations, which solves the main objection against local provenancing, i.e. negative effects of genetic drift and low genetic variability resulting in low adaptability to environmental change.

The main idea behind regional admixture provenancing is that populations within such regions experience similar environmental conditions. Although older meta-analyses of local adaptation did not reveal relationship between geographic distance and strength of local adaptation (Leimu and Fischer 2008), a number of recent, well-designed studies found regional adaptation, i.e. a geographically broader analogue of local adaptation for several species as well for entire communities (Becker et al. 2006; Ellis et al. 2007; Raabová et al. 2007, 2011; Weißhuhn et al. 2012; Volis et al. 2015; Hamann et al. 2016). Patterns of regional adaptation have also been observed between pathogens or herbivores and their host plants (Laine 2005; Kalske et al. 2016).

Despite regional adaptation, individual populations within the regions are still genetically differentiated on smaller spatial scale because of spatial isolation, habitat fragmentation, physical barriers and restricted gene flow determined by species traits (Michalski and Durka 2012; Reisch and Bernhardt-Römermann 2014). Consequently, the mixing of seeds from different populations within a region will increase genetic variation compared to single seed sources. At the same time, restricting seed transfers to within a region will protect historical genetic patterns that exist on larger spatial scales, especially if the regions are delineated taking the biogeography of a given species into account (Taberlet et al. 1998).

The mixing of seeds will likely reduce inbreeding. In fragmented landscapes, it might be necessary to collect seeds from rather small and possibly inbred populations. Using seeds from individual small populations for restoration may lead to poor plant performance, but the mixing of seed from several populations can reduce inbreeding depression and result in healthy progeny in the next generation (Hufford et al. 2012; Frankham 2015).

Some environmental factors, such as microclimate, soil moisture or nutrient supply, vary locally rather than regionally and are thus often associated with local adaptation at small spatial scales (Lenssen et al. 2004; Gimenez-Benavides et al. 2007). In such cases, seeds should ideally be sourced from natural populations where the conditions match the target locality as closely as possible. This is, however, often unrealistic. Therefore, a mixture of seeds from several populations within a region will likely provide diverse material adapted to a range of environmental factors, and at least some of the seeds will come from a population with an environment not too dissimilar from the target locality. The idea is that such a regional mixture should be useful for the majority of common microenvironments within a region.

The key component of regional admixture provenancing is delineating the regions. In principle, the requirements for regions are the same as for seed transfer zones. They share similar environmental characteristics, so that seeds can be transferred with no or neglible detrimental effects on mean population fitness (Hufford and Mazer 2003). The delineation of seed transfer zones has been subject of intense research. Some authors suggest using ecoregions (Miller et al. 2011), others rely on climatic zones (Bower et al. 2014), genecological studies of individual species (Bradley St Clair et al. 2013 and reference therein) or a combination thereof (Gibson and Nelson 2017). The optimal size of a seed transfer zone is of course species-specific. Large seed zones of several hundreds or thousands of square kilometres may be sufficient for species with frequent long-distance gene flow, such as wind-pollinated or wind-dispersed ones. In contrast, species with short-distance gene flow require much smaller seed zones. In extreme cases of rare, highly specialized species, regional admixture provenancing may not be suitable at all.

Regional admixture provenancing aims to provide material for restoration of genetically variable, yet regionally adapted populations of foundation species. Genetic variability is essential for future adaptation to environmental changes including climatic change. Such adaptation from standing genetic variation is likely to take place quickly in herbaceous plants with short generation time. However, in long-lived plants like trees the offspring of the currently planted regional generation will probably be maladapted to the future climate because climate is changing rapidly (Aitken and Bemmels 2016). To some extent, this will be buffered by high phenotypic plasticity of trees (Petit and Hampe 2006). Further, most tree have populations with high within-population genetic diversity and they produce a lot of seeds of which many germinate, but only few reach canopy height (Petit and Hampe 2006). The huge amount of genetically diverse seedlings provides tremendous material for natural selection, and only individuals that are the most suited to the novel environment will survive, allowing for adaptation. However, for the most extreme climate scenarios, it is not clear whether this will be sufficient and predictive provenancing (assisted migration) might be considered necessary. Unfortunately, as discussed above, the direct evidence that predictive provenancing will indeed have the desired effect is limited, probably because plants are adapted not only to climate, but also to other aspects of the environment (Ennos 2015; Kranabetter et al. 2015; Sinclair et al. 2015). Further research is needed to find the optimal seed sourcing strategy for plants with long generation times in the era of climate change.

Regional admixture provenancing in practice

Regional admixture provenancing as described here has been developed for non-woody plants in Germany (Prasse et al. 2010). It consists of two major elements: (1) a map of seed transfer zones (region of origin) and (2) seed zone-specific species lists. The system was regularized by the Ordinance on Preservation mixtures (ErMiV 2011) which implements the EU Directive 2010/60, and which has already been adopted in several certified seed production systems (e.g. RegioZert®, VWW-Regiosaaten®).

The basis for the delineation of the seed transfer zones was the German system of 89 natural regions (Meynen and Schmithüsen 1953) which have been identified based on geomorphology, geology, hydrology and soil. These natural regions were then grouped together based on pedogenic rock, soil type and climate. For the climate factors, specific emphasis was given to isotherms (0 °C in January, 18 °C in July), annual rainfall (< 500, 500–700, 700–100, > 1000 mm), and oceanity (18 °C difference between mean summer and mean winter temperature as threshold for oceanic versus continental climate). This reduced the number of units to 50, which were then further aggregated based on geomorphology (e.g. mountain ranges, large plateaus) and environmental similarity based on expert opinion, and a rule that, to protect natural patterns of intraspecific biodiversity, a resulting seed zone was not allowed to exceed 400 km extent in either direction. The exact borders of the seed zones were discussed with the administration of the federal states of Germany to ensure practical feasibility. This procedure resulted in a final number of 22 seed transfer zones (Herkunftsregionen, Fig. 2). It is important to note that in this system seed transfer zones are not species-specific, but are, to simplify practical implementation, equally applied to all species.

Fig. 2

Map of seed transfer zones and production areas in Germany. Thin lines indicate 22 seed transfer zones (‘Herkunftsregion’, i.e. region of origin), thick lines indicate eight production areas (Prasse et al. 2010)

The developed system of seed transfer zones is used together with a criteria-based ‘species filter’ that selects species suitable for general use in each seed transfer zone. The starting point of species selection is a list of all non-woody species that occur within a given seed zone. From these, the filter excludes all species that are either rare, endangered (Red listed), hybrids, neoendemics, taxonomically problematic, or species that have their range edge within the zone (further details in Prasse et al. 2010). The final lists were cross-checked by regional experts and agreed upon with German authorities. To facilitate easy application of the concept, an interactive website allows generating species lists for each seed transfer zone, and provides maps of the borders of seed zones (http://www.regionalisierte-pflanzenproduktion.de).

To supply the market with regional seeds adapted to wider range of environmental conditions, the German system demands to collect seeds from at least five large populations across a seed transfer zone (Prasse et al. 2010). The seeds are then mixed and either directly used or, more frequently, propagated as crops on fields and sold for restoration projects (Rieger et al. 2014). To make the propagation practically feasible, the 22 seed transfer zones are currently grouped into eight production areas (Fig. 2), with the idea that while regional seeds can be used only within a given seed transfer zone and must be propagated separately for each, the propagation itself can take place anywhere within the given production area. This enables a single farmer to produce seeds from several seed transfer zones on the same farm and allows development of a profitable business. The farm-produced seeds are available for restoration projects in a given seed zone, or are used for re-establishing another generation of cultivation. This procedure can be repeated for up to five generations, afterwards the seed production must start from a new wild collection (for details see Prasse et al. 2010).

The market with regional seeds (‘Regiosaatgut’) is now well-established in Germany and it is growing rapidly, with the demand often exceeding the supply (pers. comm. of all major German seed producers). The seed producing companies offer a range of region-specific standard seed mixtures for different habitat types, but they also allow customer-defined mixtures, or sell seeds of individual wild species. Currently, the use of regional seeds is only required in conservation projects or ecological compensation measures, but the seeds are often used for standard greening, too. However, from May 2020 onwards the use of regional seeds will be mandatory for all seeding in the open landscape outside of forests and agriculture. Recent studies indicate that the existing regional seed system in Germany is meaningful. Plants grown from commercially available seeds are indeed often regionally adapted (Bucharova et al. 2017b), their genetic structure often corresponds to the seed transfer zones (Durka et al. 2017), and propagation across several generations does not lead to strong phenotypic or genetic alterations (Nagel et al. unpublished).

The strategy of regional admixture provenancing could be easily applied also in other countries, especially if they already have established systems of seed transfer zones, as is the case e.g. for the USA, Canada or the UK (e.g. Bower et al. 2014), http://www.prt.com/products-services/seed-zone-maps, http://www.forestry.gov.uk/pdf/FRMGuidelinesRoPmap.pdf. In these countries, regional provenancing could be adopted with little additional cost as modification of the current system, when seed material from multiple populations within one seed zone will be mixed and the regional mixture will be used for restoration purposes. As regional admixture provenancing relies on seeds from a region, its application in other countries will depend on whether systems for obtaining local or regional seeds exist. In countries where local provenancing is a common practice, regional admixture can be used as simple mixing of seeds from number of local populations (Havens et al. 2015). On the other hand, restoration challenges are sometimes so huge that the demand for seeds vastly exceeds their availability (Merritt and Dixon 2011). For example, in 2017 alone, wild-fires in USA destroyed around 40 million ha (http://www.nifc.com), and much of this land would be taken over by invasive species if not actively restored (Mealor et al. 2012). In such cases, lack or regional seeds should not halt restoration projects, and any source of native seeds including native cultivars will help to prevent further land degradation.

Conclusions

We propose regional admixture provenancing as a compromise strategy that reconciles natural adaptation and historical patterns of genetic diversity with the need to provide sufficient genetic variability for populations to be able to adapt to changing environments. The strategy is supported by experimental data, both under current and future conditions (Bucharova et al. 2016a, 2017b). However, so far the data come from one habitat type only—European mesic grasslands—and it is therefore not clear whether the strategy will be successful also in other habitats or on other continents. It is a common problem with novel seed-sourcing strategies that there is a lack of robust experimental tests. So far the only strategy that is supported by data on a global scale is local provenancing, with unambiguous evidence for local adaptation as a general pattern (Oduor et al. 2016). However, theoretical predictions suggest that strict local provenancing may not be suitable under future environmental changes. Consequently, we need to find compromise strategies, and we need in particular well-designed and carefully analysed field experiments (Bucharova et al. 2017a; Breed et al. 2018) and genomic analyses (Durka et al. 2017; Williams et al. 2014) to identify the best seed-sourcing strategies for ecosystem restoration in a changing world.

Notes

Acknowledgements

AB thanks the Bridging Funds Program of University Tübingen for financing her stay at that institute.

References

  1. Abdala-Roberts L, Marquis RJ (2007) Test of local adaptation to biotic interactions and soil abiotic conditions in the ant-tended Chamaecrista fasciculata (Fabaceae). Oecologia 154:315–326CrossRefPubMedGoogle Scholar
  2. Aitken SN, Bemmels JB (2016) Time to get moving: assisted gene flow of forest trees. Evol Appl 9:271–290CrossRefPubMedGoogle Scholar
  3. Alexander JM, Diez JM, Levine JM (2015) Novel competitors shape species’ responses to climate change. Nature 525:515–518CrossRefPubMedGoogle Scholar
  4. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16:743–753CrossRefGoogle Scholar
  5. Aubry C, Shoal R, Erickson V (2005) Grass cultivars: their origins, development, and use on national forests and grasslands in the Pacific Northwest. USDA Forest ServiceGoogle Scholar
  6. Barbour MA, Fortuna MA, Bascompte J et al (2016) Genetic specificity of a plant-insect food web: implications for linking genetic variation to network complexity. Proc Natl Acad Sci USA 113:2128–2133CrossRefPubMedGoogle Scholar
  7. Baudis M, Ellerbrock RH, Felsmann K et al (2014) Intraspecific differences in responses to rainshelter-induced drought and competition of Fagus sylvatica L. across Germany. For Ecol Manage 330:283–293CrossRefGoogle Scholar
  8. Becker U, Colling G, Dostal P et al (2006) Local adaptation in the monocarpic perennial Carlina vulgaris at different spatial scales across Europe. Oecologia 150:506–518CrossRefPubMedGoogle Scholar
  9. Beierkuhnlein C, Thiel D, Jentsch A et al (2011) Ecotypes of European grass species respond differently to warming and extreme drought. J Ecol 99:703–713CrossRefGoogle Scholar
  10. Bischoff A, Trémulot S (2011) Differentiation and adaptation in Brassica nigra populations: interactions with related herbivores. Oecologia 165:971–981CrossRefPubMedGoogle Scholar
  11. Bjorkman AD, Vellend M, Frei ER, Henry GHR (2017) Climate adaptation is not enough: warming does not facilitate success of southern tundra plant populations in the high Arctic. Glob Chang Biol 23:1540–1551CrossRefPubMedGoogle Scholar
  12. Bossdorf O, Auge H, Lafuma L et al (2005) Phenotypic and genetic differentiation between native and introduced plant populations. Oecologia 144:1–11CrossRefPubMedGoogle Scholar
  13. Bower AD, Bradley St Clair J, Erickson V (2014) Generalized provisional seed zones for native plants. Ecol Appl 24:913–919CrossRefPubMedGoogle Scholar
  14. Bradley St Clair J, Kilkenny FF, Johnson RC et al (2013) Genetic variation in adaptive traits and seed transfer zones for Pseudoroegneria spicata (bluebunch wheatgrass) in the northwestern United States. Evol Appl 6:933–948CrossRefPubMedPubMedCentralGoogle Scholar
  15. Breed MF, Stead MG, Ottewell KM et al (2013) Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conserv Genet 14:1–10CrossRefGoogle Scholar
  16. Breed MF, Harrison PA, Bischoff A et al (2018) Priority actions to improve provenance decision making. Bioscience (in press)Google Scholar
  17. Broadhurst LM, Lowe A, Coates DJ et al (2008) Seed supply for broadscale restoration: maximizing evolutionary potential. Evol Appl 1:587–597PubMedPubMedCentralGoogle Scholar
  18. Bucharova A (2017) Assisted migration within species range ignores biotic interactions and lacks evidence. Restor Ecol 25:14–18CrossRefGoogle Scholar
  19. Bucharova A, Durka W, Hermann J-M et al (2016a) Plants adapted to warmer climate do not outperform regional plants during a natural heat wave. Ecol Evol 6:4160–4165CrossRefPubMedPubMedCentralGoogle Scholar
  20. Bucharova A, Frenzel M, Mody K et al (2016b) Plant ecotype affects interacting organisms across multiple trophic levels. Basic Appl Ecol 17:688–695CrossRefGoogle Scholar
  21. Bucharova A, Durka W, Hölzel N et al (2017a) Are local plants the best for ecosystem restoration? It depends on how you analyze the data. Ecol Evol 7:10683–10689CrossRefPubMedPubMedCentralGoogle Scholar
  22. Bucharova A, Michalski S, Hermann J-M et al (2017b) Genetic differentiation and regional adaptation among seed origins used for grassland restoration: lessons from a multi-species transplant experiment. J Appl Ecol 54:127–136CrossRefGoogle Scholar
  23. Byrne M, Yeates DK, Joseph L et al (2008) Birth of a biome: insights into the assembly and maintenance of the Australian arid zone biota. Mol Ecol 17:4398–4417CrossRefPubMedGoogle Scholar
  24. Crémieux L, Bischoff A, Müller-Schärer H, Steinger T (2010) Gene flow from foreign provenances into local plant populations: fitness consequences and implications for biodiversity restoration. Am J Bot 97:94–100CrossRefPubMedGoogle Scholar
  25. Crowe KA, Parker WH (2008) Using portfolio theory to guide reforestation and restoration under climate change scenarios. Clim Change 89:355–370CrossRefGoogle Scholar
  26. De Kort H, Vandepitte K, Honnay O (2013) A meta-analysis of the effects of plant traits and geographical scale on the magnitude of adaptive differentiation as measured by the difference between QST and FST. Evol Ecol 27:1081–1097CrossRefGoogle Scholar
  27. Dounavi A, Netzer F, Celepirovic N et al (2016) Genetic and physiological differences of European beech provenances (F. sylvatica L.) exposed to drought stress. For Ecol Manage 361:226–236CrossRefGoogle Scholar
  28. Durka W, Michalski SG, Berendzen KW et al (2017) Genetic differentiation within multiple common grassland plants supports seed transfer zones for ecological restoration. J Appl Ecol 54:116–126CrossRefGoogle Scholar
  29. Edmands S (2007) Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol Ecol 16:463–475CrossRefPubMedGoogle Scholar
  30. Ellis AG, Weis AE, Gaut BS (2007) Spatial scale of local adaptation and population genetic structure in a miniature succulent, Argyroderma pearsonii. New Phytol 174:904–914CrossRefPubMedGoogle Scholar
  31. Elmendorf SC, Henry GHR, Hollister RD et al (2015) Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns. Proc Natl Acad Sci USA 112:448–452CrossRefPubMedGoogle Scholar
  32. Ennos RA (2015) Resilience of forests to pathogens: an evolutionary ecology perspective. Forestry 88:41–52CrossRefGoogle Scholar
  33. ErMiV (2011) Regulation about the placing on the market of seed of conservation mixtures. Bundesgesetzblatt I 65:2641–2646Google Scholar
  34. Espeland EK, Emery NC, Mercer KL et al (2017) Evolution of plant materials for ecological restoration: insights from the applied and basic literature. J Appl Ecol 54:102–115CrossRefGoogle Scholar
  35. Etterson JR (2004) Evolutionary potential of Chamaecrista fasciculata in relation to climate change. II. Genetic architecture of three populations reciprocally planted along an environmental gradient in the great plains. Evolution 58:1459CrossRefPubMedGoogle Scholar
  36. Fernández M, Gil L, Pardos JA (1999) Response of Pinus pinaster Ait. provenances at early age to water supply. I. Water relation parameters. Ann For Sci 56:179–187CrossRefGoogle Scholar
  37. Frankham R (2015) Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol Ecol 24:2610–2618CrossRefPubMedGoogle Scholar
  38. Frankham R, Ballou JD, Eldridge MDB et al (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475CrossRefPubMedGoogle Scholar
  39. Garrido E, Andraca-Gómez G, Fornoni J (2012) Local adaptation: simultaneously considering herbivores and their host plants. New Phytol 193:445–453CrossRefPubMedGoogle Scholar
  40. Gellie NJC, Breed MF, Thurgate N et al (2016) Local maladaptation in a foundation tree species: implications for restoration. Biol Conserv 203:226–232CrossRefGoogle Scholar
  41. Ghilarov AM (2000) Ecosystem functioning and intrinsic value of biodiversity. Oikos 90:408–412CrossRefGoogle Scholar
  42. Gibson A, Nelson CR (2017) Comparing provisional seed transfer zone strategies for a commonly seeded grass, Pseudoroegneria spicata. Nat Areas J 37:188–199CrossRefGoogle Scholar
  43. Gilhaus K, Vogt V, Hölzel N (2015) Restoration of sand grasslands by topsoil removal and self-greening. Appl Veg Sci 18:661–673CrossRefGoogle Scholar
  44. Gimenez-Benavides L, Escudero A, Iriondo JM (2007) Local adaptation enhances seedling recruitment along an altitudinal gradient in a high mountain Mediterranean plant. Ann Bot 99:723–734CrossRefPubMedPubMedCentralGoogle Scholar
  45. Gray LK, Gylander T, Mbogga MS et al (2011) Assisted migration to address climate change: recommendations for aspen reforestation in western Canada. Ecol Appl 21:1591–1603CrossRefPubMedGoogle Scholar
  46. Hamann E, Kesselring H, Armbruster GFJ et al (2016) Evidence of local adaptation to fine- and coarse-grained environmental variability in Poa alpina in the Swiss Alps. J Ecol 104:1627–1637CrossRefGoogle Scholar
  47. Hancock N, Hughes L (2014) Turning up the heat on the provenance debate: testing the “local is best” paradigm under heatwave conditions. Austral Ecol 39:600–611CrossRefGoogle Scholar
  48. Harvey E, Gounand I, Ward CL, Altermatt F (2017) Bridging ecology and conservation: from ecological networks to ecosystem function. J Appl Ecol 54:371–379CrossRefGoogle Scholar
  49. Havens K, Vitt P, Still S et al (2015) Seed sourcing for restoration in an era of climate change. Nat Areas J 35:122–133CrossRefGoogle Scholar
  50. Herrera AM, Carruthers RI, Mills NJ (2011) No evidence for increased performance of a specialist psyllid on invasive French broom. Acta Oecol 37:79–86CrossRefGoogle Scholar
  51. Hölzel N, Buisson E, Dutoit T (2012) Species introduction: a major topic in vegetation restoration. Appl Veg Sci 15:161–165CrossRefGoogle Scholar
  52. Hufford KM, Mazer SJ (2003) Plant ecotypes: genetic differentiation in the age of ecological restoration. Trends Ecol Evol 18:147–155CrossRefGoogle Scholar
  53. Hufford KM, Krauss SL, Veneklaas EJ (2012) Inbreeding and outbreeding depression in Stylidium hispidum: implications for mixing seed sources for ecological restoration. Ecol Evol 2:2262–2273CrossRefPubMedPubMedCentralGoogle Scholar
  54. Hull-Sanders HM, Clare R, Johnson RH, Meyer GA (2007) Evaluation of the evolution of increased competitive ability (EICA) hypothesis: loss of defense against generalist but not specialist herbivores. J Chem Ecol 33:781–799CrossRefPubMedGoogle Scholar
  55. Jay F, Manel S, Alvarez N et al (2012) Forecasting changes in population genetic structure of alpine plants in response to global warming. Mol Ecol 21:2354–2368CrossRefPubMedGoogle Scholar
  56. Jones TA (2003) The restoration gene pool concept: beyond the native versus non-native debate. Restor Ecol 11:281–290CrossRefGoogle Scholar
  57. Kadu CAC, Konrad H, Schueler S et al (2013) Divergent pattern of nuclear genetic diversity across the range of the Afromontane Prunus africana mirrors variable climate of African highlands. Ann Bot 111:47–60CrossRefPubMedPubMedCentralGoogle Scholar
  58. Kalske A, Muola A, Laukkanen L et al (2012) Variation and constraints of local adaptation of a long-lived plant, its pollinators and specialist herbivores. J Ecol 100:1359–1372CrossRefGoogle Scholar
  59. Kalske A, Leimu R, Scheepens JF, Mutikainen P (2016) Spatiotemporal variation in local adaptation of a specialist insect herbivore to its long-lived host plant. Evolution 70:2110–2122CrossRefPubMedGoogle Scholar
  60. Kebede M, Ehrich D, Taberlet P et al (2007) Phylogeography and conservation genetics of a giant lobelia (Lobelia giberroa) in Ethiopian and Tropical East African mountains. Mol Ecol 16:1233–1243CrossRefPubMedGoogle Scholar
  61. Keller M, Kollmann J, Edwards PJ (1999) Palatability of weeds from different European origins to the slugs Deroceras reticulatum Müller and Arion lusitanicus Mabille. Acta Oecol 20:109–118CrossRefGoogle Scholar
  62. Kettenring KM, Mercer KL, Reinhardt Adams C, Hines J (2014) Application of genetic diversity-ecosystem function research to ecological restoration. J Appl Ecol 51:339–348CrossRefGoogle Scholar
  63. Kiehl K, Kirmer A, Shaw N, Tischew S (2014) Guidelines for native seed production and grassland restoration. Cambridge Scholar PublishingGoogle Scholar
  64. Kramer AT, Wood TE, Frischie S, Havens K (2018) Considering ploidy when producing and using mixed-source native plant materials for restoration. Restor Ecol 26:13–19CrossRefGoogle Scholar
  65. Kranabetter JM, Stoehr M, O’Neill GA (2015) Ectomycorrhizal fungal maladaptation and growth reductions associated with assisted migration of Douglas-fir. New Phytol 206:1135–1144CrossRefPubMedGoogle Scholar
  66. Laine A-L (2005) Spatial scale of local adaptation in a plant-pathogen metapopulation. J Evol Biol 18:930–938CrossRefPubMedGoogle Scholar
  67. Laukkanen L, Leimu R, Muola A et al (2012) Plant chemistry and local adaptation of a specialized folivore. PLoS ONE 7:e38225CrossRefPubMedPubMedCentralGoogle Scholar
  68. Leger EA, Baughman OW (2015) What seeds to plant in the Great Basin? Comparing traits prioritized in native plant cultivars and releases with those that promote survival in the field. Nat Areas J 35:54–68CrossRefGoogle Scholar
  69. Leger EA, Forister ML (2005) Increased resistance to generalist herbivores in invasive populations of the California poppy (Eschscholzia californica). Divers Distrib 11:311–317CrossRefGoogle Scholar
  70. Leimu R, Fischer M (2008) A meta-analysis of local adaptation in plants. PLoS ONE 3:e4010CrossRefPubMedPubMedCentralGoogle Scholar
  71. Leimu R, Mutikainen P, Koricheva J, Fischer M (2006) How general are positive relationships between plant population size, fitness and genetic variation? J Ecol 94:942–952CrossRefGoogle Scholar
  72. Leimu R, Muola A, Laukkanen L et al (2012) Plant-herbivore coevolution in a changing world. Entomol Exp Appl 144:3–13CrossRefGoogle Scholar
  73. Lenssen JPM, van Kleunen M, Fischer M, de Kroon H (2004) Local adaptation of the clonal plant Ranunculus reptans to flooding along a small-scale gradient. J Ecol 92:696–706CrossRefGoogle Scholar
  74. Lesica P, Allendorf FW (1999) Ecological genetics and the restoration of plant communities: mix or match? Restor Ecol 7:42–50CrossRefGoogle Scholar
  75. Linhart YB, Grant MC (1996) Evolutionary significance of local genetic differentiation in plants. Annu Rev Ecol Syst 27:237–277CrossRefGoogle Scholar
  76. Lu P, Parker WH, Cherry M et al (2014) Survival and growth patterns of white spruce (Picea glauca [Moench] Voss) rangewide provenances and their implications for climate change adaptation. Ecol Evol 4:2360–2374CrossRefPubMedPubMedCentralGoogle Scholar
  77. Matías L, González-Díaz P, Jump AS (2014) Larger investment in roots in southern range-edge populations of Scots pine is associated with increased growth and seedling resistance to extreme drought in response to simulated climate change. Environ Exp Bot 105:32–38CrossRefGoogle Scholar
  78. McDonald T, Jonson J, Dixon KW (2016) National standards for the practice of ecological restoration in Australia. Restor Ecol 24:S4–S32CrossRefGoogle Scholar
  79. McKay JK, Christian CE, Harrison S, Rice KJ (2005) “How local is local?”: a review of practical and conceptual issues in the genetics of restoration. Restor Ecol 13:432–440CrossRefGoogle Scholar
  80. McLane SC, Aitken SN (2012) Whitebark pine (Pinus albicaulis) assisted migration potential: testing establishment north of the species range. Ecol Appl 22:142–153CrossRefPubMedGoogle Scholar
  81. Mealor BA, Cox S, Booth DT (2012) Postfire Downy Brome (Bromus tectorum) invasion at high elevations in Wyoming. Invasive Plant Sci Manag 5:427–435CrossRefGoogle Scholar
  82. Merritt DJ, Dixon KW (2011) Restoration seed banks-a matter of scale. Science 332:424–425CrossRefPubMedGoogle Scholar
  83. Meynen E, Schmithüsen J (1953) Handbuch der naturräumlichen Gliederung Deutschlands. Selbstverlag der Bundesanstalt für Landeskunde, Bad GoldesbergGoogle Scholar
  84. Michalski SG, Durka W (2012) Assessment of provenance delineation by genetic differentiation patterns and estimates of gene flow in the common grassland plant Geranium pratense. Conserv Genet 13:581–592CrossRefGoogle Scholar
  85. Miller SA, Bartow A, Gisler M et al (2011) Can an ecoregion serve as a seed transfer zone? Evidence from a common garden study with five native species. Restor Ecol 19:268–276CrossRefGoogle Scholar
  86. Mody K, Collatz J, Bucharova A, Dorn S (2017) Crop cultivar affects performance of herbivore enemies and may trigger enhanced pest control by coaction of different parasitoid species. Agric Ecosyst Environ 245:74–82CrossRefGoogle Scholar
  87. Montwé D, Isaac-Renton M, Hamann A, Spiecker H (2016) Drought tolerance and growth in populations of a wide-ranging tree species indicate climate change risks for the boreal north. Glob Chang Biol 22:806–815CrossRefPubMedGoogle Scholar
  88. Oduor AMO, Leimu R, van Kleunen M (2016) Invasive plant species are locally adapted just as frequently and at least as strongly as native plant species. J Ecol 104:957–968CrossRefGoogle Scholar
  89. Petit RJ, Hampe A (2006) Some evolutionary consequences of being a tree. Annu Rev Ecol Evol Syst 37:187–214CrossRefGoogle Scholar
  90. Prach K, Fajmon K, Jongepierová I, Řehounková K (2015a) Landscape context in colonization of restored dry grasslands by target species. Appl Veg Sci 18:181–189CrossRefGoogle Scholar
  91. Prach K, del Moral R (2015b) Passive restoration is often quite effective: response to Zahawi et al. (2014). Restor Ecol 23:344–346CrossRefGoogle Scholar
  92. Prasse R, Kunzmann D, Schröder R (2010) Development and practical implementation of minimal requirements for the verification of origin of native seeds of herbaceous plants (in German). In Cooperation with Verband Deutscher Wildsamen- und Wildpflanzenproduzenten. DBU, reference no. 23931Google Scholar
  93. Prober SM, Byrne M, McLean EH et al (2015) Climate-adjusted provenancing: a strategy for climate-resilient ecological restoration. Front Ecol Evol 3:65CrossRefGoogle Scholar
  94. Raabová J, Münzbergová Z, Fischer M (2007) Ecological rather than geographic or genetic distance affects local adaptation of the rare perennial herb, Aster amellus. Biol Conserv 139:348–357CrossRefGoogle Scholar
  95. Raabová J, Münzbergová Z, Fischer M (2011) The role of spatial scale and soil for local adaptation in Inula hirta. Basic Appl Ecol 12:152–160CrossRefGoogle Scholar
  96. Reisch C, Bernhardt-Römermann M (2014) The impact of study design and life history traits on genetic variation of plants determined with AFLPs. Plant Ecol 215:1493–1511CrossRefGoogle Scholar
  97. Rieger ER, Feucht BI, Wieden MA (2014) Agricultural progagation of native seeds and development of a certification procedure in Germany. In: Kiehl K, Kirmer A, Shaw N, Tischew S (eds) Guidelines for native seed production and grassland restoration. Cambridge Scholars Publishing, pp 101–116Google Scholar
  98. Schreiber SG, Ding C, Hamann A et al (2013) Frost hardiness vs. growth performance in trembling aspen: an experimental test of assisted migration. J Appl Ecol 50:939–949CrossRefGoogle Scholar
  99. Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68:1–15CrossRefPubMedGoogle Scholar
  100. Sgrò CM, Lowe AJ, Hoffmann AA (2011) Building evolutionary resilience for conserving biodiversity under climate change. Evol Appl 4:326–337CrossRefPubMedGoogle Scholar
  101. Sinclair FH, Stone GN, Nicholls JA et al (2015) Impacts of local adaptation of forest trees on associations with herbivorous insects: implications for adaptive forest management. Evol Appl 8:972–987CrossRefPubMedPubMedCentralGoogle Scholar
  102. Taberlet P, Fumagalli L, Wust-Saucy AG, Cosson JF (1998) Comparative phylogeography and postglacial colonization routes in Europe. Mol Ecol 7:453–464CrossRefPubMedGoogle Scholar
  103. Taeger S, Zang C, Liesebach M et al (2013) Impact of climate and drought events on the growth of Scots pine (Pinus sylvestris L.) provenances. For Ecol Manage 307:30–42CrossRefGoogle Scholar
  104. Taeger S, Sparks TH, Menzel A (2015) Effects of temperature and drought manipulations on seedlings of Scots pine provenances. Plant Biol 17:361–372CrossRefPubMedGoogle Scholar
  105. Tahmasebi Z, Mohammadi H, Arimura G et al (2014) Herbivore-induced indirect defense across bean cultivars is independent of their degree of direct resistance. Exp Appl Acarol 63:217–239CrossRefPubMedGoogle Scholar
  106. Taïbi K, del Campo AD, Aguado A, Mulet JM (2016) Early establishment response of different Pinus nigra ssp. salzmanii seed sources on contrasting environments: implications for future reforestation programs and assisted population migration. J Environ Manage 171:184–194CrossRefPubMedGoogle Scholar
  107. Taïbi K, del Campo AD, Vilagrosa A et al (2017) Drought tolerance in Pinus halepensis seed sources as identified by distinctive physiological and molecular markers. Front Plant Sci 8:1202CrossRefPubMedPubMedCentralGoogle Scholar
  108. Thiel D, Kreyling J, Backhaus S et al (2014) Different reactions of central and marginal provenances of Fagus sylvatica to experimental drought. Eur J For Res 133:247–260CrossRefGoogle Scholar
  109. Toju H, Sota T (2006) Imbalance of predator and prey armament: geographic clines in phenotypic interface and natural selection. Am Nat 167:105–117CrossRefPubMedGoogle Scholar
  110. Turchetto-Zolet AC, Pinheiro F, Salgueiro F, Palma-Silva C (2013) Phylogeographical patterns shed light on evolutionary process in South America. Mol Ecol 22:1193–1213CrossRefPubMedGoogle Scholar
  111. Varone L, Vitale M, Catoni R, Gratani L (2016) Physiological differences of five Holm oak (Quercus ilex L.) ecotypes growing under common growth conditions were related to native local climate. Plant Species Biol 31:196–210CrossRefGoogle Scholar
  112. Vergeer P, Sonderen E, Ouborg NJ (2004) Introduction strategies put to the test: local adaptation versus heterosis. Conserv Biol 18:812–821CrossRefGoogle Scholar
  113. Volis S, Ormanbekova D, Yermekbayev K et al (2015) Multi-approaches analysis reveals local adaptation in the emmer wheat (Triticum dicoccoides) at macro- but not micro-geographical scale. PLoS ONE 10:e0121153CrossRefPubMedPubMedCentralGoogle Scholar
  114. Weißhuhn K, Prati D, Fischer M, Auge H (2012) Regional adaptation improves the performance of grassland plant communities. Basic Appl Ecol 13:551–559CrossRefGoogle Scholar
  115. Wellstein C, Cianfaglione K (2014) Impact of extreme drought and warming on survival and growth characteristics of different provenences of juvenile Quercus pubescens Willd. Folia Geobot 49:31–47CrossRefGoogle Scholar
  116. Whittet R, Cavers S, Cottrell J, Ennos R (2016) Seed sourcing for woodland creation in an era of uncertainty: an analysis of the options for Great Britain. Forestry 90:163–173Google Scholar
  117. Wilczek AM, Cooper MD, Korves TM, Schmitt J (2014) Lagging adaptation to warming climate in Arabidopsis thaliana. Proc Natl Acad Sci USA 111:7906–7913CrossRefPubMedGoogle Scholar
  118. Williams AV, Nevill PG, Krauss SL (2014) Next generation restoration genetics: applications and opportunities. Trends Plant Sci 19:529–537CrossRefPubMedGoogle Scholar
  119. Zytynska SE, Preziosi RF (2011) Genetic interactions influence host preference and performance in a plant-insect system. Evol Ecol 25:1321–1333CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Plant Evolutionary Ecology, Institute of Evolution and EcologyUniversity of TübingenTübingenGermany
  2. 2.Nature Conservation and Landscape EcologyUniversity of FreiburgFreiburg im BreisgauGermany
  3. 3.Biodiversity and Ecosystem Research Group, Institute of Landscape EcologyUniversity of MünsterMünsterGermany
  4. 4.Restoration Ecology, Department of Ecology and Ecosystem ManagementTechnical University of MünchenMunichGermany
  5. 5.Norwegian Institute of Bioeconomy Research (NIBIO)ÅsNorway
  6. 6.Institute of Environmental PlanningLeibniz University HannoverHanoverGermany
  7. 7.Department of Community EcologyHelmholtz Centre for Environmental Research-UFZHalleGermany
  8. 8.German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzigGermany
  9. 9.Biodiversity and Ecosystem Research Group, Institute of Landscape EcologyUniversity of MünsterMünsterGermany

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