Relative effects of management and environmental conditions on performance and survival of populations of a terrestrial orchid, Dactylorhiza majalis
Introduction
The survival of populations of many European orchid species is strongly dependent on appropriate site management, especially regular mowing or grazing (Waite and Hutchings, 1991, Lind, 1992, Kull, 2002). Effect of management on orchid populations is best assessed by long-term monitoring (Wells and Cox, 1989, Wells and Cox, 1991, Vanhecke, 1991, Willems and Bik, 1991, Falb and Leopold, 1993, Sieg and King, 1995, Gill, 1996, Inghe and Tamm, 1988, Wells et al., 1998, Brzosko, 2002, Janečková and Kindlmann, 2002, Jersáková et al., 2002, Kindlmann and Balounová, 2001, Øien and Moen, 2002, Tali, 2002, etc.). However, when such data are analysed, it is crucial to disentangle the effects of weather and management (usually mowing) from the intrinsic orchid dynamics – which is what we concentrate on here.
When effect of weather is considered, usually several months’ average temperatures and/or precipitation totals are compared with some characteristics of plant performance, such as percentage of flowering plants, flowering shoot height or leaf area. Results of such studies have been conflicting: some studies have confirmed the intuitive assumption that weather does affect plant performance (Wells, 1981, Firmage and Cole, 1988, Wells and Cox, 1989, Wells and Cox, 1991, Willems and Bik, 1991, Vanhecke, 1991, Wells et al., 1998, Sieg and King, 1995, Brzosko, 2002, Janečková and Kindlmann, 2002), while others have not (Whigham and O’Neill, 1991, Wheeler et al., 1998, Falb and Leopold, 1993, Øien and Moen, 2002). The problem may stem from the fact that the weather effects on orchid behaviour may be associated with some relatively short extreme conditions (e.g. short periods of severe drought or frost), which may cause severe damage to the population (Vanhecke, 1991). This raises the question of whether the often-used temperature means and/or precipitation totals spanning several months are appropriate for detecting weather influences on orchid populations. If short periods of extreme weather matter, then they may be obscured in long-term averages. Therefore, we test here the impact in such analyses of the time period over which weather variables are analysed.
Even if mowing is generally considered beneficial to the fitness of meadow orchids (Kull, 2002), its occurrence does not automatically ensure persistence of orchid populations (Tamm, 1991). Therefore, we study here, how plant fitness is affected by various mowing regimes. We use the total leaf area of a plant and its flower stalk height as indicators of plant fitness, because leaf area determines the plant’s decision to flower or remain sterile in the next year in orchids (Wells et al., 1998, Kindlmann and Balounová, 1999, Kindlmann and Balounová, 2001) and the amount of stored carbohydrates in the underground storage organs for the next year (Kindlmann and Balounová, 1999), and because the height of the flower stalk is significantly correlated with the number of flowers and ultimately with the number of seeds in the current year (Kindlmann and Balounová, 2001). Consequently, leaf area and flower stalk height are closely correlated with two main fitness components: number of seeds in the current year and the size of the next-year’s tuber.
There are two mechanisms, how mowing can affect orchid performance. Early mowing (usually in July, immediately after maturation of orchid seed capsules and seed dispersal), can suppress their competitors – dominant grasses (Willems, 1990, Lepš, 1999). Late mowing (∼ August, September) removes the old plant biomass, thus reduces shading of orchids in the subsequent year and increases light available for photosynthesis (Lepš, 1999). This may be especially important for many temperate orchids species which require high light conditions and grow early in the season (Kull, 2002). Therefore we study here, whether both mechanisms are important in management of our study species: which co-occurring species are characteristic for presence and absence of mowing and how shading affects various aspects of orchid performance, like leaf area and shape, seed weight, and length of the flower stalk.
We use Dactylorhiza majalis, which commonly occurs in wet meadows in central Europe, as our study species. Although the absolute number of extant D. majalis sites is not low, their rate of decline is worrisome. The main reasons for this decline are believed to include agricultural practices and the period of collectivisation (transformation of small-scale private farms into large-scale agricultural co-operatives) accompanied by large inputs of fertilizers, drainage, conversion of meadows and pastures into arable land and cessation of both cattle and sheep grazing in sub-montane regions (Wotavová et al., 2004). Thus it is not only the present number of sites, but also the temporal trend in the number of sites that determines this species’ “rarity” (Wotavová et al., 2004). Recently, attempts have been made to restore wet meadows by blocking their drainage and reintroducing original plant species, including D. majalis. It is therefore important to determine, what is the correct management regime at such sites. Therefore we make here proposals for correct management of this species, which is the practical contribution of this paper for conservation of D. majalis.
Section snippets
The species studied
The western-marsh orchid (D. majalis Reichenb. Hunt et Summerh) is the most abundant species in the genus Dactylorhiza in Central Europe. The rapid decline of its natural habitats has caused it to be considered an endangered species in the Czech Republic (Wotavová et al., 2004). D. majalis has a broad ecological niche and occurs in wet to damp meadows, fenlands, wetlands and peatlands. D. majalis tolerates slightly acidic to strongly alkaline soils (pH 5.2–8.1). Its leaves appear above ground
Climatic conditions
Results of the GLM models with LA(t) as a response are shown in Table 2. LA(t) was always best fitted by LA(t − 1), which explained most of the variation (deviance reduction = 42.7%). The next best predictors differed with the length of the time interval over which the climatic variables were averaged. When 3 months averages were considered, the correlation between LA(t) and the next best predictor, the sum of precipitations from April to June, was negative. Correlations between the second best
Climatic conditions
In all models, leaf area in the previous year was always by far the best predictor of the leaf area in the current year – much better than any of the climatic variables considered. This is in accord with many previous studies (Kindlmann and Balounová, 1999, Kindlmann and Balounová, 2001, Janečková and Kindlmann, 2002 and references therein) and implies that last year’s leaf area affects the current year’s leaf area via carbohydrates stored as reserves in the tubers (Kindlmann and Balounová, 1999
Acknowledgments
This work has been supported by the grant 206/03/H034 of the GA ČR. We thank Mike Hutchings, Rich Shefferson, Tiiu Kull, Pete Carey, eight anonymous reviewers and many other unnamed colleagues for valuable comments on the earlier versions of this manuscript.
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