Impact of management and habitat on demographic traits of Primula vulgaris in an agricultural landscape

- IMPACT OF MANAGEMENT AND HABITAT ON PRIMULA VULGARIS IN AN AGRICULTURAL LANDSCAPE - 171

AbstractQuestion: Is demographic performance of Primula vulgariscorrelated with habitat characteristics of the small landscapeelements in which it occurs? Can we use this species as anindicator for species-rich semi-natural habitats?Location: Flanders, Belgium.Methods: To capture differences in demographic traits andhabitat characteristics, both within and between populations, atwo-level survey was carried out. Population size and struc-ture of 89 P. vulgaris populations in different types of smalllandscape elements was recorded in 1999. At plot level, densi-ties of different life stages were determined and these wererelated to edaphic conditions and vegetation structure andcomposition.Results: Three different population types were distinguished:(1) dynamic populations, characterized by seedling and juve-nile proportions, (2) normal populations with relatively moreadults, but with considerable numbers of seedlings and juve-niles and (3) senescent populations, mainly consisting ofadults. Senescent populations were significantly smaller thanpopulations with a dynamic demographic structure. At plotlevel, comparison of demographic characteristics betweendifferent management regimes revealed that recruitment ratesand total plant density of P. vulgaris were highest in plots thatreceived a regime that included mowing and clearing of ditchbanks whereas densities were lower along forest edges. Forthese plots, it was shown that nutrient levels were higher.Densities of adults as well as juvenile and seedling densitieswere negatively correlated with vegetation height.Conclusions: Local disturbance and heterogeneity may maskthe relationship between unfavourable conditions and demo-graphic characteristics at population level, but it is clear that insmall populations recruitment needs to be lifted to guaranteeits persistence. Performance of P. vulgaris in small landscapeelements can be a first indication of plant species diversity insmall landscape elements.

Keywords: Landscape element; Management regime; Multi-ple-scale approach; Population structure, Target species; Veg-etation structure.

Nomenclature: Lambinon et al. (1998).

Impact of management and habitat on demographic traits of Primula vulgaris in an agricultural landscape

Endels, Patrick1*; Jacquemyn, Hans1; Brys, Rein1,2 & Hermy, Martin1

1Laboratory for Forest, Nature and Landscape Research, University of Leuven, Vital Decosterstraat 102, B-3000 Leuven,Belgium; 2Institute of Nature Conservation, Kliniekstraat 25, B-1070 Brussels, Belgium; *Corresponding author;Fax +3216329760; E-mail [email protected]; http://www.agr.kuleuven.ac.be/lbh/lbnl/ecology

Introduction

With the intensification of agriculture, many origi-nally rather widespread species are now confined tosemi-natural habitat remnants (e.g. Stoate et al. 2001;Robinson & Sutherland 2002). As other regions in WEurope, the Flemish agricultural landscape, originallycharacterized by a dense network of small landscapeelements, e.g. hedgerows, ditches and rows of pollardtrees), has been subjected to radical changes in the lastfew decades. As in many other regions in NW Europe,remnant semi-natural structures are progressively elimi-nated in the process of lot enlargement and land consoli-dation. In addition, small landscape elements are oftenprone to radical changes in management and nutrientconditions leading to lower habitat quality (e.g. Kleijn& Verbeek 2000). In many cases, populations of speciesin small landscape elements are directly exposed toinfluences as direct fertilizer and herbicide misplacement.As a result of reduced habitat quality, plant diversity isexpected to decrease (Garbutt & Sparks 2002; Blomqvistet al. 2003).

Relatively few studies (e.g. Eisto et al. 2000; Lennarts-son 2002; Vergeer et al. 2003) have explored the exactmechanisms that govern dynamics and viability of plantpopulations of plant species under these circumstances.However, deterministic factors such as habitat deterio-ration or destruction may only be the first step in proc-esses leading to population decline. For small popula-tions, an elevated risk of local extinction is connected tothe fact that the impact of demographic, genetic andenvironmental stochasticity becomes more importantwith decreasing population size (e.g. Lande 1988;Ellstrand 1992). Demographic stochasticity, i.e. ran-dom deviations in survival and reproduction of indi-viduals from expected population means, may changesex ratios or proportions of style morphs in populations(e.g. Kéry et al. 2003). Together with isolation, this canlead to reduced pollination (Jennersten 1988; Matsumura& Washitani 2000). Other biotic interactions such aplant-pathogen and plant-herbivore relations (e.g. Lienert

Applied Vegetation Science 7: 171-182, 2004© IAVS; Opulus Press Uppsala.

172 ENDELS, P. ET AL.

& Fischer 2003) may also be altered in ways that mayaffect individual plant performance. While environmentalstochasticity was initially considered to be the mostimportant threat to population persistence (Boyce 1992),recent studies have shifted attention towards geneticprocesses (e.g. Luijten et al. 2000; Fischer et al. 2000;Vergeer et al. 2003). Small, isolated populations arelikely to suffer from loss of genetic variation and fixa-tion of deleterious alleles due to genetic drift (Boyce1992; Ellstrand & Elam 1993). Furthermore, inbreedingdepression can lower individual fitness and populationviability (Young et al. 1996).

Demographic studies can reveal the critical stages inthe life cycle of rare plants and – if the results are relatedto different management practices and the structure andcomposition of the surrounding vegetation – they mayallow a prediction of the effects of management changeson threatened populations (Hutchings 1991). Detaileddemographic studies, however, often take many years tocomplete, which hampers their use in conservation biol-ogy on a broad scale (Harvey 1985). Therefore, a lesstime consuming method to relate demographic perform-ance of populations to vegetation or management is theanalysis of population structure in a range of plantcommunities or under different management regimes.The structure of a population may be described byclassifying individuals either by age, size or their lifestage (Rabotnov 1969). However, in practice it is oftenimpossible to establish the age of individual plants,except by following them from germination onwards.Recently, methods were developed that enable directage determination of herbaceous perennial herbs (herbchronology by counting rings in the secondary rootxylem; Dietz & Ullmann 1998), but it is still a laborioustask to determine the age structure of several popula-tions. Moreover, especially in perennial plant species,both size and reproductive capacity are poorly corre-lated with actual age (Harper 1977). Therefore, an alter-native way to describe populations of such species in asingle census is by determination of relative proportionsof individuals in the different ontogenetic stages in thelife cycle, generally called ‘age states’ (sensu Ooster-meijer et al. 1994). This method was used to assesspopulation viability of Gentiana pneumonanthe in wetheathlands and hay meadows (Oostermeijer et al. 1994,1996). Bühler & Schmid (2001) applied the same tech-nique to investigate the impact of management regimeand altitude on populations of Succisa pratensis in east-ern Switzerland. Population structure of Salvia pratensisin Dutch dry floodplain grasslands was also related tomanagement type and vegetation structure (Hegland et al.2001). These studies all succeeded in distinguishing threedifferent types of populations: ‘normal’ (or ‘stable’)populations with a relatively high proportion of adults

but still a considerable number of young individuals,‘dynamic’ populations characterized by a large propor-tion of seedlings and juveniles, and ‘senescent’ (or‘regressive’) populations in which rejuvenation wasscarce and which were dominated by (large) adults.Moreover, in all these cases population type could in-deed be related to site conditions (both structure andcomposition of the surrounding vegetation and/or dis-turbance regime), indicating that this method can yielduseful results concerning the conservation and manage-ment of remaining populations of threatened species.

Primula vulgaris is a rare and declining species inFlanders. Most of the remaining populations are rathersmall and restricted to ditch banks or forest edges (Endelset al. 2002a). Previous research also showed the species’vulnerability for changes in land use (Endels et al.2002a). The development of several population traitsseemed to be correlated with original population sizeand surrounding land use, indicating that (1) small popu-lations are prone to extinction and (2) the ultimate fateof a population depends to a certain extent on the localmanagement regime or – better – a lack thereof. In thispaper we investigate the possibility whether semi-natu-ral elements can function as a refuge for this vulnerableplant species. With this respect, the following questionswere addressed:1. Is it possible to detect differences in population struc-ture in relic populations of Primula vulgaris and – if thisis the case – can different population types (see above)be distinguished? Is there a link between populationstructure and population size?2. What is the relationship between population structureand habitat type, management regime and vegetationstructure at population and plot (within-population) level?3. Can P. vulgaris be considered as a target species? Inother words: what is the relationship between the species’performance and vegetation composition of the herba-ceous layer in small landscape elements?

The study was based on a multiple-scale approach,designed to capture differences in age-state spectra bothwithin and between populations. By investigating bioticand abiotic interactions on these two levels we aimed atdisentangling the relationships between current demo-graphic status, local habitat conditions and characteris-tics of the surrounding agricultural matrix. From a view-point of conservation and management, it is also inter-esting to test whether different population structuresoccur in vegetation types that differ in structure andcomposition. If this is the case, then the age structure forpopulations of P. vulgaris (and possibly also of otherspecies with a similar life cycle) could be used as a basisfor evaluating and predicting the effects of differentmanagement strategies (Oostermeijer et al. 1994).


Material and Methods

Species and study sites

Primula vulgaris is a small, long-lived, herbaceous,diploid perennial with a North Atlantic and Mediterra-nean distribution (Hegi 1975; Hultén & Fries 1986). InFlanders, it is considered a very rare and decliningspecies: only 89 populations, ranging in size from 1 to1219 individuals were found in 1999. Most populations(58 in total or 68%) contained less than 50 individuals(Endels et al. 2002a). Although P. vulgaris is regardedas a typical woodland species (Whale 1984) associatedwith newly opened gaps (Valverde & Silvertown 1995),most populations (85%) in Flanders occur along ditchbanks along hedgerows and forest edges (Endels et al.2002a). It forms basal rosettes that lose their old leavesduring autumn; new leaves are produced in late winter.Plants flower in early spring (March-May) and are mainlypollinated by Hymenoptera (mostly bumblebees) andDiptera (Woodell 1960; Boyd et al. 1990; Kucharczyck& Teske 1996). As most other species in the genusPrimula, P. vulgaris shows a genetically based self-incompatible (distylous) breeding system (Richards1997). As a consequence, only pollination between pinand thrum morphs will result in efficient seed set, al-though under certain circumstances various rates ofself-fertilization may occur (Woodell 1960; Ornduff1979; Bodmer 1984). Seeds ripen around the middle ofJune. They have an elaiosome which is attractive to antsand rodents. These may actively harvest seeds and inthat way act as dispersers (Richards 1984; Valverde &Silvertown 1995). However, in reality, they almost al-ways fall directly on the ground in the immediate vicin-ity of the mother plant (barochory) (Cahalan & Gliddon1985). Vegetative spread is restricted and only occurswithin very short distances to the mother plant throughthe production of lateral rosettes. Although these indi-vidual rosettes can die off, individual plants are rela-tively long-lived (10 - 30 yr; Boyd et al. 1990).

The study sites are all found within a 20 km radiusaround Bruges. Geographically, they are situated justsouth of the border between the polder landscape and amore sandy region in the south (see Endels et al. 2002afor a detailed map). This resulted in a a high variabilityin soil variables within very short distances. Soil tex-ture, for example, varied from coarse sand to fine sandyloam. A high disturbance rate of the field margins andditch bank habitat added to this large variance in (top)soilcharacteristics (see Table 1). While agricultural use hasbeen less intensive than in the polder region when itcomes to herbicide and fertilizer use, there has been anincreasing conversion of permanent pastures to fields ofmaize (Zea mais) and a few other crops (potatoes, veg-

etables) during recent decades. Together with a strongurbanization around the city of Bruges, this processadded to the hostility of the landscape matrix for vulner-able plant species which increases the importance ofsmall landscape elements as a refuge for P. vulgaris andother species with a formerly larger distribution area. Inthe process of lot enlargement, the ecological networkmainly consisting of lines of trees, hedgerows, ditchesand small forest patches was often damaged. Full char-acterisation of the sites are presented by Endels et al.(2002a).

Measurements

Primula vulgaris populations were located accord-ing to the methods described in Endels et al. (2002a).

In the early spring of 1999, P. vulgaris individualswere counted on all sites (ditch banks, forest edges,hedgerows and little drains in deciduous forest patches).The ontogeny of plants may be divided into periods orstages based on the rise and extinction of the reproductivefunction and on certain juvenile and adult characteristics(Gatsuk et al. 1980). Since the actual age of individualplants of P. vulgaris could not be determined, the popula-tion structure analysis was based on the number of indi-viduals in the different life stages of the plant whichroughly relate to age (Oostermeijer et al. 1994). Wedistinguished four categories based on morphology andreproductive status: seedlings, juveniles, vegetative adultsand reproductive adults (see Endels et al. 2002a).

Measurements at the population levelOn population level (Fig. 1), observations were made

on several habitat characteristics: habitat type, surround-ing land use, possibility of grazing from adjoining pas-ture, exposition and inclination of the site (details inEndels et al. (2002a). Along the population, soil sam-ples were taken at random with an auger of 3.5 cmdiameter down to 20 cm depth, subsequent to removingthe litter layer. After homogenising the composite sam-ples per site (population), pH, organic carbon and the

Table 1. Descriptive statistics of the soil samples taken in 66populations of Primula vulgaris (except for C and pH, unit forall variables is mg/100g; SD = standard deviation).

Minimum Maximum Mean SD

C (%) 1.2 5.5 3.0 1.2pH 4.0 7.5 5.6 0.8P 1 38 13.2 9.5K 6 46 19.5 9.6Mg 3 48 15.765 9.0Ca 26 1143 186.7 166.2Na 1.2 10.6 3.8 1.9N (Kjeldahl) 107 489 224.7 75.0


Fig. 1. Schematic representation ofPrimula vulgaris populations (full boldlines) along a network of ditches (dashedlines) around Donk, east of Bruges) of thedata recording on two different levels. a.Population-based releves; b. Plot-basedrecordings (within populations).

nutrients P, K, Mg and Ca were determined. Using thesame method, soil samples were also collected in indi-vidual 1-m2 plots in which vegetation descriptions weremade (see further). All these samples were collectedduring the winter of 1999-2000, air-dried and passedthrough a 2 mm sieve. Carbon was determined using themodified Walkley and Black method and pH was meas-ured in a 0.1 molar KCl-solution. The nutrients wereextracted using an ammonium lactate (pH = 3.75) solu-tion. K, Mg and Ca were measured with Atomic Ab-sorption Spectrophotometry (A.A.S.), P by colorimetryand total N using the Kjeldahl-Lauro method (see fur-ther Hendrickx et al. 1992).

Measurements at the plot levelAt the plot level (Fig. 1) ten populations were sam-

pled in randomly located 1-m2 plots (n = 99). Withinthese plots composite samples of the topsoil were takenduring the same period as mentioned above. Soil sampleanalysis was executed according to the same proce-dures. During the summer of 1999 we carried out 75vegetation descriptions (in ten populations) in a subsetof all plots which were sampled for topsoil analysis.Vegetation description included determination of totalcover of the herb layer, individual cover (using the Londoscale; Londo 1976) for each species within the plot andmean vegetation height (based on five random measure-ments in each plot). Presence-absence data of all vascu-lar plant species in the herb layer are presented in App.1. Total shrub and tree cover were also estimated. Popu-lation structure censuses for these plots included count-ing of individuals in different life stages during earlyspring of 1999 (at peak flowering of P. vulgaris).

Data analysis

Analyses at the population levelK-means clustering (Legendre & Legendre 1998) was

performed on the population structure data (after calcu-lating the proportions of each life stage in every popula-tion). With this method a set of samples is divided into apre-selected number of groups by maximizing between-group relative to within-group variation (Legendre &Legendre 1998), and the result tested for overall groupeffect by MANOVA. Differences in mean demographicvariables between the population types (groups) wereexamined with Tukey post hoc tests. We compared popu-lation size between K-means clusters by one-way analy-sis of variance (ANOVA; population size log-trans-formed). In addition the relationship between populationstructure and habitat characteristics (habitat type, adjoin-ing land use, inclination and exposition of the site) wasanalysed through Pearson χ2 values for the cross-tabula-tion of both variables. Population size was compared fordifferent types of adjacent land use by a non-parametricANOVA (Kruskal-Wallis test, Siegel & Castellan 1988).To study the relationship between soil variables, wecarried out a PCA (with varimax rotation), followed bycalculation of Spearman correlation coefficients (rs) be-tween scores on the first two axes and individual soilsvariables. Subsequently, PCA axes were related to thepopulation structure by comparing the PCA scores of thesites for the three population types by an ANOVA. Thesame analysis was performed to compare soil characteris-tics for different habitat types and adjoining land uses. Asit can be expected that competitive exclusion by e.g.Phragmites australis and Urtica dioica both affects reju-venation of the targets species (and hence the population


structure) and species composition of the herb layer, wecompared values for a diversity index (the mean Shannon-Wiener diversity index; Kent & Coker 1992) betweendifferent population types by a Kruskal-Wallis test.

Analyses at the plot levelTo assess the magnitude of variation in chemical

topsoil characteristics between and within populations, amodel II ANOVA was executed with plot as a randomfactor (Sokal & Rohlf 1995). For each plot we determinedthe number of P. vulgaris individuals within each lifestage. Since densities were often low, only two categorieswere distinguished: young individuals (seedlings andjuveniles) and adults (both vegetative and reproductive).We determined the total number of species, the Shannon-Wiener diversity index and relative cover of species witha competitive plant strategy (sensu Grime et al. 1988).We also determined PCA-scores of the soil characteris-tics to investigate the relationship between individualvariables. The vegetation data (cover of herb layerspecies) were ordinated with DCA (CANOCO; ter Braak1990).

We distinguished between three habitat types differ-ing in management: (1) mown sites along ditches whichwere cleared every 3-4 yr, (2) grazed field margins and(3) ditches within forest patches or along forest edges.The influence of management on the density of differentlife stages of the target species (adults and young indi-viduals) was tested with a mixed nested ANOVA (popu-lation nested in management type) using type III sums ofsquares. It was impossible to use proportions of P. vulgarislife stages (as we did for the population level) for theseanalyses since assumptions for ANOVA were not met inthat case (and no non-parametric alternative for nestedANOVA is available). Densities were log-transformed tomeet assumptions on homogeneity of variance and nor-mal distribution of the residuals. For the comparison ofseedling and juvenile densities between management re-gimes, we incorporated adult density as a covariable inthe nested ANOVA as it can be expected that moreyounger individuals are located in the immediate vicinityof adults (due to short distance dispersal, see earlier underdescription of the species).

The same analysis was performed to compare topsoilcharacteristics (axes with eigenvalues > 1 derived fromPCA with soil variables), vegetation composition (firsttwo DCA axes), species diversity (Shannon-Wiener in-dex) and vegetation structure (total cover of herb layer,relative cover of competitive species and mean vegeta-tion height). Plots without P. vulgaris were omitted toallow for parametric ANOVA. This did not alter theoutcome of the nested ANOVAs as preliminary analysesshowed that there were no differences in topsoil charac-teristics or vegetation structure and diversity between

plots with P. vulgaris plants and plots without individualsof the target species. Finally, both the soil and vegetationmetavariables were correlated with local (within plot)population density and structure. To avoid problems withpseudoreplication (Hurlbert 1984), we used the meandensities in the plots for each site and the means of thevegetation and soil characteristics. All statistical analyseswere executed in SPSS version 11.0 (Anon. 2001).

Results

Effects of habitat characteristics on population sizeand structure at the population level

Current population age-state spectraUsing K-means clustering, three main groups of

populations – about equal in size – with clearly differentage-state structures (Wilks’ λ = 0.08; F = 57.6, P < 0.001)were distinguished (Fig. 2). Selection of other numbersof groups for the analysis resulted either in too heteroge-neous clusters (in the case of two pre-selected groups) orentire clusters consisting of only a few, deviating popu-lations (in the case of four and five groups). On thethree-group level, the first type (consisting of ‘dynamic’populations) is characterized by larger proportions ofyounger life stages relative to the adult stages (Fig. 2).This is the opposite of the second type of populationstructure: these ‘senescent’ populations have very lowproportions of individuals in younger life stages. The‘normal’ populations are situated somewhat in betweenthese two types: there are still seedlings and juvenilespresent in the population but they mainly have highlevels of flowering adults. Non-flowering adults’ pro-portions are also higher than in the other groups.

Fig. 2. Age-state structure of groups of populations withdifferent demographic structures as determined by K-meansclustering (n = 89) and results of Tukey post-hoc tests for thethree different population types. Characters indicate if meansdiffer significantly at α = 0.05.


Impact of habitat characteristics on age-state spectraand population size

By comparison of log transformed population size inone-way ANOVA, it was demonstrated that there was asignificant difference between the three defined popula-tion types (F = 4.506, P = 0.014). Dynamic populationswere significantly larger than populations that showed asenescent population structure (Fig. 3a). While a mar-ginally significant relationship between population typeand adjoining land use was found (Pearson χ2 = 9.267, n= 89, P = 0.055), no link with habitat type, exposition orinclination of the site could be detected. Dynamic popu-lations are relatively more common along pastures (ob-served: 13; expected: 8). More senescent populationsthan expected are located next to arable fields (ob-served: 10; expected: 6) and a normal population struc-ture is relatively more found in or along forests (ob-served: 10; expected: 7). Adjacent land use also had asignificant impact on population size (Kruskal-Wallisχ2 = 10.32, n = 89, P = 0.007): populations surroundedby forest were significantly larger than those next toarable fields (Fig. 3b).

Principal components analysis of soil variables indi-cated that almost all nutrients (N, P, K, Na, Mg) wereassociated with the first axis and that the second axisrepresented soil acidification (significantly correlated withCa content and pH, Table 2). Therefore, we will furtherrefer to these axes as eutrophication (PCA axis 1) andalkalinization (PCA axis 2). Using the scores on thesePCA axes, no differences in soil characteristics werefound between population types (Table 3a). However,soil characteristics did significantly differ between habi-tat types and adjoining land uses (Table 3b, c). Tukeypost-hoc tests showed that nutrient levels (PCA axis 1)were higher and soils were more acid (PCA axis 2) onbanks of little drains in forests or along forest edgescompared to ditch banks without trees (Fig. 4a). When thedata set was split up based on surrounding land use (Fig.4b), higher eutrophication values were found in forestscompared to sites surrounded by grasslands and arablefields. Sites in forest also had lower pH, Mg and Cavalues. At population level there were no significant

differences in vegetation diversity (Shannon-Wienerindex) between population types (Kruskal-Wallis χ2 =2.673; n = 10; P = 0.263).

Impact of management regime on P. vulgaris densityand habitat characteristics at plot level

By executing a model II ANOVA to assess themagnitude of variation in chemical topsoil characteris-tics between and within populations, it appeared that theplot level significantly adds to the variance in all soilcharacteristics (Table 4). With variance split up be-tween and within populations, it was shown that 20%(% organic carbon) to 46% (N-Kjeldahl) was due towithin population (i.e. between plots) variance. Theresults of principal components analysis of soil vari-ables on plot level generally corresponded with those ofthe earlier analysis on population level. The first axiswas again associated with nutrient levels, while lowvalues on PCA axis 2 were once more related withacidification (Table 5). Density of the target specieswithin the plots differed between the three managementregimes, but a population effect was only found for thedensity of the young individuals (mixed nested ANOVA,

Fig. 3. a. Results of Tukey post-hoc tests followingANOVA to explore differences in population sizebetween population types as determined by K-meansclustering of population demographic structure (n =89); b. results of post-hoc tests on non-parametricANOVA (Kruskal-Wallis test) to compare populationsize between different types of adjacent land use (n =89). Characters indicate if means differ significantlyat α = 0.05.

Table 2. PCA (with varimax rotation) of composite soil sam-ples for 66 populations of Primula vulgaris: Spearman corre-lation coefficients (rs) are calculated to investigate the rela-tionship between individual soil variables and PCA axes 1 and2 with an associated eigenvalue > 1.

(Eigenvalues) PCApop_AX1 PCApop_AX2(3.746) (1.714)

Log (%C) 0.896 *** –0.018Log (N) 0.862 *** 0.173Log (K) 0.759 *** 0.170Log (Na) 0.724 *** 0.019Log (P) 0.428 *** 0.220Log (Mg) 0.559 *** 0.654 ***

Log (Ca) 0.303 * 0.857 ***

Log (pH-KCl) –0.155 0.906 ***

log: variable was log transformed to meet normal distribution stand-ards; * 0.01 < P < 0.05; *** P < 0.001.


Table 3. ANOVA on differences in soil characteristics beweenand within groups (PCA with soil variables, axes 1 and 2; seeTable 2) , between (a) population types, (b) habitat types and(c) different adjoining land uses.

df Mean Square F

(a) Population typesPCApop_AX1 Between-group 2 1.36 1.38

Within-group 63 0.99Total 65

PCApop_AX2 Between-group 2 0.07 0.07Within-group 63 1.03Total 65

(b) Habitat typePCApop_AX1 Between-group 3 4.334 5.168**


PCApop_AX2 Between-group 3 3.793 4.385**


(c) Adjoining land usePCApop_AX1 Between-group 2 4.166 4.632*


PCApop_AX2 Between-group 2 3.171 3.406*


* 0.01 < P < 0.05, ** 0.001 < P < 0.01.

Fig. 4. Results of post-hoc Tukey tests for ANOVA in Table3, to investigate the effect of (a) habitat type and (b) adjoiningland use on soil characteristics at the population level (PCAwith soil variables, axes 1 and 2; see Table 2). Means ± SD aregiven; letters indicate if means differ significantly at α = 0.05.

Table 4. Analysis of the variance between and within sites forchemical characteristics of topsoil samples of ten locationswhere Primula vulgaris was found. ANOVA model II was usedsince samples were taken in randomly selected plots withinpopulations (cf. Fig. 1). F-values significant at P < 0.001.

F Added Var (%) Var (%)var component between within

Log %C 34.31 0.627 79.77 20.23Log(Humus) 10.13 0.013 51.84 48.16Log(P) 15.18 0.079 62.59 37.41Log(K) 7.83 0.021 44.59 55.41Log(Mg) 15.07 0.034 62.43 37.57Log(Ca) 17.30 0.062 65.78 34.22Log(Na) 11.34 0.017 54.93 45.07Log(N) 10.97 0.007 54.06 45.94

Table 5. Spearman correlation coefficients (rs) between axes1 and 2 of a Principal components analysis (PCA, with varimaxrotation) of soil samples for 87 plots in ten Primula vulgarispopulations with associated eigenvalue > 1.

(Eigenvalues) PCAplot_AX1 PCAplot_AX2(3.657) (1.792)

Log (%C) 0.800 *** 0.166Log (Na) 0.791 *** –0.056Log (P) 0.779 *** 0.126Log (K) 0.716 *** 0.135Log(Kjell-N) 0.667 *** 0.391 *

Log (Ca) 0.213 * 0.936 ***

Log (pH) –0.027 0.867 ***

Log (Mg) 0.232 * 0.821 ***

Variables were log transformed to meet normal distribution standards;* 0.01 < P < 0.05; ** 0.001 < P < 0.01; *** P < 0.001.

Table 6a). While total P. vulgaris density and densitiesof seedlings and juveniles were highest at sites with amowing-clearing regime (Fig. 5a), post-hoc tests re-vealed no difference in adult densities. Comparison oftopsoil characteristics showed that there was a differ-ence between management types (Table 5b). However,both eutrophication (PCA axis 1) and alkalisation (PCAaxis 2) also depended on the population where plotswere located. Plots in forest patches and forest edgeshad the highest nutrient levels and lowest pH (Fig. 5b).Grazed field margins and ditches showed high valuesfor PCA axis 2. Nutrient levels were lowest for mownsites along ditches that were cleared every 3 - 4 yr (Fig.5b) and they were negatively correlated with seedlingand juvenile densities of P. vulgaris (Pearson correla-tion coefficient rp = – 0.677, n = 10, P = 0.031; densitylog-transformed). While there was a clear populationeffect (Table 6c), no differences in vegetation composi-tion (DCA axes 1 and 2) and diversity (Shannon-Wienerindex) between management regimes could be detected(Fig. 5c). Nevertheless, when linked to seedling andjuvenile density of P. vulgaris individuals (densitieswere log transformed), it was revealed that there was asignificant relationship with Shannon-Wiener index (rp= 0.676; n =10; P = 0.034) and total number of specieswithin the plot (rp = 0.692; n = 10; P = 0.027). Variablesthat represented vegetation structure on the other hand,were significantly affected by management. Total cover


Discussion

Current demographic status at the population leveland its relationship with environmental variables

Previous research on P. vulgaris in an agriculturallandscape showed that small populations are prone toextinction (Endels et al. 2002a). It was demonstratedthat changes in population size and other demographictraits were associated with intensification of surround-ing land use and initial population size. To investigate towhat extent demographic performance was related togenetic variation and structure, allelic richness, observedand expected heterozygosity, Wright’s inbreeding coef-ficient and FST were compared among populations withdifferent demographic properties (Jacquemyn et al.2003). No difference in any of these measures amongpopulation types was found, indicating that factors otherthan genetic diversity and structure were involved indetermining demographic performance of P. vulgarispopulations. On the other hand, it was revealed thatduring a period of 13 yr relative imbalances in pin/thrum ratios increased while mean population size de-creased significantly (Endels et al. 2002b). Unequalmorph frequencies may disrupt pollination, which, inturn, can lead to a higher variance in fruit and seed set(Jacquemyn et al. 2001; Kéry et al. 2003). For P. vulgarisit was shown that a deviation from equal morph ratioshad a negative effect on fruit and seed production(Jacquemyn et al. 2003; Brys et al. 2004). In contrastwith earlier findings for other species that plant per-formance is lower in small populations (e.g. Kéry et al.2000; Mavraganis & Eckert 2001; Vergeer et al. 2003),this study also revealed that reproductive output signifi-cantly decreased at low plant densities. This result againadds to the thesis that reproductive output might bepollination-limited for this species, as could be con-cluded for the related Primula sieboldii (Washitani et al.1994; Matsumura & Washitani 2000). However, directeffects of management regime and edaphic conditionson the demographic structure of P. vulgaris populationshave received only little attention up till now.

We found three distinct population types: dynamic,normal and senescent, in accordance with Oostermeijeret al. (1994, Gentiana pneumonanthe), and Hegland etal. (2001, Salvia pratensis) Colling et al. (2002) couldonly distinguish between two population types inScorzonera humilis (regenerating and aged). For P.vulgaris, a strong relationship with population size wasshown (Fig. 3a). More important – for conservation andmanagement purposes – than the comparison of simpledemographic properties may be the link with environ-mental variables. Relating population structure to ad-joining land use for P. vulgaris revealed that senescent

Table 6. Mixed nested ANOVA (type III sum of squares) –population (pop) nested in management type (man) – to checkthe effect of management on (a) Density (log transformed) ofPrimula vulgaris individuals within 1-m2 plots; (b) Topsoilcharacteristics in a PCA with soil variables, axes 1 and 2; seeTable 5; (c) Species diversity according to Detrended Corre-spondence Analysis, axes 1 and 2; diversity and structure. Plotswithout P. vulgaris omitted. See Fig. 5 for abbreviations.

df MS F

(a) Densities of target speciesDENS_S+J Man Hypothesis 2 3.02 4.87 *

Error 8.46 0.62Pop (man) Hypothesis 7 0.90 5.91 ***

Error 38 0.15DENS_A Hypothesis 1 0.05 0.28

Error 30 0.17

DENS_A Man Hypothesis 2 0.50 4.00 *

Error 10.99 0.13Pop (man) Hypothesis 7 0.12 0.89

Error 55 0.14DENS_T Man Hypothesis 2 1.79 4.78 *


Error 62 0.22

(b) Topsoil characteristicsPCAplot_AX1 Man Hypothesis 2 11.01 3.85 *


Error 62 0.53PCAplot_AX2 Man Hypothesis 2 12.38 6.60 *


Error 62 0.25

(c) Species diversity / Vegetation structureDCA_AX1 Man Hypothesis 2 209187.81 1.61


Error 62 1553.87

DCA_AX2 Man Hypothesis 2 55959.26 0.96Error 7.01 58154.78

Pop (man) Hypothesis 7 60146.79 44.60 ***

Error 62 1348.60S-W Man Hypothesis 2 0.30 0.89


Error 62 0.01TotCover Man Hypothesis 2 1207.15 19.78 **


Error 62 57.49

CoverC Man Hypothesis 2 71.85 6.53 *


Error 62 0.73VegHght Man Hypothesis 2 15225.45 5.48 *

Error 7.33 2776.13Pop (man) Hypothesis 7 2968.77 4.03 **

Error 62 736.41* 0.01 < P < 0.05; ** 0.001 < P < 0.01; *** P < 0.001.

of the herb layer and relative cover of competitivespecies were lowest at sites in forest or along forestedges (Fig. 5d), but no correlation with density of thetarget species could be found. Vegetation height waslowest for the mowing-clearing regime compared toother management types and was significantly corre-lated with P. vulgaris density (seedling and juveniledensity: rp = –0.684; n =10; P = 0.029; adult density: rp= –0.838; n = 10; P = 0.002).


populations were relatively more common along arablefields, whereas dynamic populations occurred in sitesfrequently disturbed by, e.g., grazing and mowing.

Current demographic status at plot level and itsrelationship with environmental variables

Similarly, at the plot level, highest seedling andjuvenile densities were found at sites with a mowing-clearing management regime. Reduced germination andearly seedling mortality could be the main path resultingin senescent population structure under deterioratingconditions. It became clear that within-population vari-ance in edaphic conditions was rather important (Table4). This may not be a surprise, given the high disturb-ance rates in small landscape elements and it can be thereason that there was only a significant relationshipbetween population traits and soil variables at plot level(Table 6b). Field evidence (Endels pers. obs.) suggeststhat the somewhat higher proportion of juveniles insenescent populations may be due to individuals thathave been present for some time but are not able to growinto the adult life stage due to suboptimal conditions.These findings are confirmed by earlier transplantationexperiments, where P. vulgaris seedling die-off washighest next to arable fields compared to sites withadjoining grassland or forest (Jacquemyn et al. 2003).However, this may primarily be a competition effect (cf.Jacquemyn et al. 2003) rather than a direct response toincreased nutrient levels since the latter were higher inforest patches (Fig. 4). Indeed, it emerged that (1) cover

Fig. 5. Post-hoc Tukey tests for nested ANOVA(Table 6) on the effect of management on (a) thedensity (log transformed ) of P. vulgaris indi-viduals within 1-m2 plots: DENS_S+J = seed-lings and juveniles, DENS_A: adults, DENS_T:all individuals): (b) topsoil characteristics in aPCA with soil variables, axes 1 and 2; see Table5; (c) Vegetation composition according toDetrended Correspondence Analysis axes 1 and2 and diversity; S-H = Shannon-Wiener index;(d) Vegetation structure; TotCover = total coverof herb layer (%), CoverC = relative cover ofcompetitive species (%); VegHght = mean veg-etation height (cm). Plots without P. vulgarisomitted. Means ± SD are given; characters indi-cate if means differ significantly at α = 0.05.

of competitive species and vegetation height was lower inplots that were mown and cleared (Fig. 5d) and (2)rejuvenation was negatively linked with vegetation height.

As to the reproductive performance of adults, thereis evidence that fruit and seed production significantlydecrease at low plant densities (Jacquemyn et al. 2003;Brys et al. 2004), although no direct relationship be-tween habitat characteristics and reproductive charac-teristics was found. Density of P. vulgaris, as mentionedearlier, can be linked to management regime and vege-tation structure. Hence, as Blomqvist et al. (2003) stated,it is likely that negative effects associated with agricul-tural land use (increased competition by more nitro-philous species or ruderals due to nutrient enrichmentand herbicide drift) can affect population persistence.To reverse these effects, grazing or clearing may pre-vent competitive exclusion by large plants such asPhragmites australis or Urtica dioica. Lindborg & Ehrlén(2002) referred to positive effects of grazing on popula-tion performance of P. farinosa, and Tamm (1972)recorded similar results for the related P. veris. Clearingof ditches and associated scraping of ditch banks inparticular, may create gaps, which provide safe sites forestablishment (cf. McConnaughay & Bazzaz 1987).While shelter by neighbouring plants appeared to benecessary for the establishment of P. veris (Ryser 1993),it is well documented that Primula species more or lessdepend on bare ground for successful germination (Tamm1972; Whale 1984; Milberg 1994). But then, why doesthe species display lower performance in forest (Fig.5a), where ground vegetation is more open (Fig. 5d)?


The low densities in plots located along forest edgesor within forest patches might be due to light conditions:Valverde & Silvertown (1995) demonstrated that seedset and seedling establishment diminished significantlywith increasing canopy closure. Soil acidification canalso explain why seedling and juvenile densities arelower in forests. Mean pH values in these plots is 5.12(SD = 0.65), which is still above the lower limit fornormal growth and establishment for P. vulgaris (pH4.7) recorded by Helliwell (1980) and Whale (1984),but may hamper optimal germination in some cases.

Primula vulgaris as an indicator for plant speciesdiversity

A floristic change is anticipated by demographicchanges, especially of perennial plants that rely on re-cruitment by seeds (Bakker et al. 1996). Hence, if thedifferences in age structure can be related to differencesin their environment, then such ‘structural demographies’can be used as a tool in vegetation monitoring in addi-tion to conventional vegetation analysis (Oostermeijeret al. 1994). Bühler & Schmid (2001) formulated somegeneral rules for the use of indicator species in vegeta-tion monitoring. First, the target species should be acharacteristic member of the plant community of inter-est and should occur steadily in high numbers at mostsites. As for P. vulgaris in our study area, the sites arerather diverse with a wide range of species that aretypical for open habitats to closed forest. Populationsare not large either (Endels et al. 2002a), but given theclear relationship with population size (Fig. 3a), popula-tion structure of even very small populations can giveuseful information about demographic status.

The normal population structure of the target speciesin a stable plant community must be known. Oostermeijeret al. (1996) could relate the three population typesfound in the wet heathland species Gentiana pneumo-nanthe to the actual population growth rates: normalpopulations were stable (finite rate of increase λ ≈ 1).

It can be concluded (Valverde & Silvertown 1997,1998) that expanding P. vulgaris populations have highrecruitment rates, which should render them ‘dynamic’.However, that study was done in plots in a forest regen-eration cycle which is very different from the situationin Flanders. Analysis of year-to-year transitions be-tween life stages for populations of P. vulgaris in smalllandscape elements with different management regimesmust reveal if the ‘stable’ population type we delineatedactually relates to populations that retain their relativeproportions of different life stages.

Several randomly distributed plots per site should besampled to partition the spatial variation among all plotsinto variance components within and between sites.

This strategy was the starting point of our study, but dueto low within plot P. vulgaris densities, it was not possibleto maintain the same measures for population structurethroughout analyses on plot and population level.

Conclusions

Our results indicate that P. vulgaris is vulnerable tohabitat deterioration associated with changes in landuse, leading to lower recruitment levels and populationsize. With decreasing population size, the significanceof stochastic processes for the dynamics and geneticcomposition of a population is assumed to increase(Pimm et al. 1988; Menges 1990, 1997). While there isno evidence for negative effects of reduced geneticdiversity or inbreeding on demographic performance onP. vulgaris populations in Flanders, demographicstochasticity may produce imbalances in morph number(Endels et al. 2002b) which, in turn, can lead to reducedpollination and seed set (Brys et al. 2004). Although thisneeds to be confirmed by a demographic analysis usingmatrix population models, the fact that senescent popu-lations were also significantly smaller than normal anddynamic populations suggests that most populations ofthis type are relic populations with a limited viability inthe long term. Integration of the results of this study withpreviously reported data on historical changes in thedistribution range of P. vulgaris in Flanders (Endels et al.2002a) suggests that under prevailing environmental con-ditions there will be a continuous decline in both thenumber and size of populations.

As far as we can see now, rapid appraisal of thepopulation structure of P. vulgaris might give an indica-tion of plant diversity of certain types of small landscapeelements (ditch banks, forest edges, hedgerows), but thespecies’ distribution is far too limited to serve as anindicator species on a large scale. Nevertheless, the pres-ence and demographic status of the species can be used toidentify small landscape elements that should be con-served preferentially. As Bühler & Schmid (2001) sug-gested, simultaneous studies of several target species ofdifferent morphology and with different life cycles mayimprove the reliability of predictions about the plantcommunity as a whole. Finally, as Bakker et al. (1996)pointed out, population or species monitoring can be usedfor the early detection of changes but it is no substitute fordetailed recordings of species composition.

Acknowledgements. This study was carried out as part of aVLINA (Flemish Impulse Program for Nature Development)project (98/03) and was supported by an IWT (Institute forpromoting scientific and technological innovation in Flan-ders) grant. We are grateful to all the farmers and landownerswho allowed us to work on their land.


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Impact of management and habitat on demographic traits of Primula vulgaris in an agricultural landscape