Guillaume Delhaye
email: guildelhaye@gmail.com

Bastien Lange
email: bastien.lange@unilasalle.fr

Michel-Pierre Faucon
email: michel-pierre.faucon@unilasalle.fr

Céline Grandjean
email: grandjean.celine@education.lu

Gregory Mahy
email: g.mahy@ulg.ac.be

Pierre Meerts
email: pmeerts@ulb.ac.

Functional traits of a broad-niched metallophyte along a toxicity gradient: disentangling intra and inter-population variation

Keywords: metal gradient, facultative metallophyte, intraspecific variation, copper availability, phenotypic plasticity

Studies on the relative contribution of inter-and intra-population trait variations in broadniched species along edaphic gradients are still scarce. Here, we explore the variation of eight traits in five populations of Cyanotis longifolia, a broad-niched metallophyte thriving on very broad gradients of soil copper concentration in Central Africa. Variation at species level was decomposed into covariation with copper, variation among sites, and residual variation. The proportion of the variance explained by the site and by the local copper gradient in each site ranged from 10 to 32% and between 5 and 51% respectively, depending on trait. At the species level, specific leaf area decreased and leaf Cu and Co concentrations increased with increasing soil Cu concentration. Surprisingly, traits related to plant size show a quadratic response with higher values on both high and low soil Cu concentrations. This could be accounted for by population-specific patterns of covariation with soil copper. Population specific traits responses were found, with some populations exhibiting no significant response while the others sometimes show opposing variation patterns. The reasons for such idiosyncratic patterns are discussed in terms of interactions with other soil factors influencing Cu availability and toxicity. In further studies on trait variation along toxicity gradients, we recommend to investigate the small scale edaphic variability and to measure traits at the population level to capture the diversity of functional responses.

Introduction

Within species variation in functional traits is now recognised as an important component of community trait variation and community assembly processes [START_REF] Siefert | A global meta-analysis of the relative extent of intraspecific trait variation in plant communities[END_REF][START_REF] Hart | How variation between individuals affects species coexistence[END_REF]. It is composed of both within population and between population variations. Most studies focus on between population trait variation along altitudinal and latitudinal gradients [START_REF] Souto | Complex bioclimatic and soil gradients shape leaf trait variation in Embothrium coccineum (Proteaceae) among austral forests in Patagonia[END_REF][START_REF] Fajardo | Intraspecific trait variation and covariation in a widespread tree species (Nothofagus pumilio) in southern Chile[END_REF] but studies comparing within species variation along several similar environmental gradients are scarce. However, growing evidence indicates that intra-population variation can be as large as inter-population variation along soil depth, light, temperature, nutrients or altitude gradients [START_REF] Bresson | To what extent is altitudinal variation of functional traits driven by genetic adaptation in European oak and beech?[END_REF][START_REF] Wellstein | Intraspecific phenotypic variability of plant functional traits in contrasting mountain grasslands habitats[END_REF][START_REF] Lemke | Patterns of phenotypic trait variation in two temperate forest herbs along a broad climatic gradient[END_REF][START_REF] Harzé | Functional traits are more variable at the intra-than inter-population level: a study of four calcareous dry-grassland plant species[END_REF][START_REF] Pfennigwerth | Trait variation along elevation gradients in a dominant woody shrub is population-specific and driven by plasticity[END_REF]. This can result from two, mutually non exclusive mechanisms i.e. phenotypic plasticity (e.g. [START_REF] Valladares | The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change[END_REF] and local genetic variation (e.g. [START_REF] Byars | Local adaptation and cogradient selection in the alpine plant, Poa hiemata, along a narrow altitudinal gradient[END_REF][START_REF] Robson | Variation in functional leaf traits among beech provenances during a Spanish summer reflects the differences in their origin[END_REF]. Both genetic and phenotypic differences of adaptive traits have been documented at very small spatial scales [START_REF] Bradshaw | Population differentiation in Agrostis tenuis Sibth[END_REF][START_REF] Linhart | Evolutionary significance of local genetic differentiation in plants[END_REF], particularly in relation to metallic trace elements (MTE) enriched soils [START_REF] Antonovics | Heavy metal tolerance in plants[END_REF][START_REF] Al-Hiyaly | The effect of zinc contamination from electricity pylons. Genetic constraints on selection for zinc tolerance[END_REF].

Soil metal contamination represents stringent selective pressure that can promote the evolution of locally adapted ecotypes [START_REF] Słomka | Increased genetic diversity of Viola tricolor L.(Violaceae) in metal-polluted environments[END_REF][START_REF] Wójcik | Physiological mechanisms of adaptation of Dianthus carthusianorum L. to growth on a Zn-Pb waste deposit-the case of chronic multimetal and acute Zn stress[END_REF]. Plant evolutionary ecology studies often compare metallicolous and non metallicolous populations, based on the assumption that the differences in ecological conditions between MTE-enriched site and the non-contaminated neighbourhood produce a clear-cut difference in plant phenotype. However, soil conditions are often extremely variable both among and within metalliferous sites which can lead to intraspecific differences in tolerance strategies, ion contents or functional traits values [START_REF] Lefebvre | Population variation and taxonomy in Armeria maritima with special reference to heavy-metal tolerant populations[END_REF][START_REF] Lange | Functional traits of a facultative metallophyte from tropical Africa: population variation and plasticity in response to cobalt[END_REF][START_REF] Stein | Relationships between soil and leaf mineral composition are element-specific, environment-dependent and geographically structured in the emerging model Arabidopsis halleri[END_REF][START_REF] Frérot | Habitat heterogeneity in the pseudometallophyte Arabidopsis halleri and its structuring effect on natural variation of zinc and cadmium hyperaccumulation[END_REF]. Although metallophytes tolerance have been extensively studied from a physiological point of view, most studies on metallophytes have focused on sites that have been recently contaminated by human activities [START_REF] Baker | Metallophytes: the unique biological resource, its ecology and conservational status in Europe, central Africa and Latin America[END_REF] and functional traits variation in metallophytes across large gradients of metal concentration in the soil remains largely understudied. Functional traits are expected to vary along toxicity gradients because of selection for optimal trait values in local environments [START_REF] Garnier | Plant functional markers capture ecosystem properties during secondary succession[END_REF] and because of trade-offs between growth or reproduction and metal tolerance [START_REF] Wilson | The cost of heavy-metal tolerance: An example[END_REF][START_REF] Ernst | Evolutionary biology of metal resistance in Silene vulgaris[END_REF] but see Harper et al 1997). Facultative metallophytes along natural MTE gradients offer excellent opportunities to study mechanisms and patterns of trait variation due to soil toxicity, which can be due to phenotypic plasticity or local genetic variation.

In South East of the DR Congo (Upper Katanga), natural outcrops of Cu-rich bedrocks occur in the form of several hundreds of "copper hills". These hills typically appear as islands of herbaceous vegetation emerging from the surrounding woodland matrix [START_REF] Duvigneaud | Cuivre et Végétation au Katanga[END_REF]. Copper concentration in the soil often increases by three orders of magnitude from the base to the top of the hill [START_REF] Séleck | Chemical soil factors influencing plant assemblages along copper-cobalt gradients: implications for conservation and restoration[END_REF]. Many other chemical and physical factors vary as well, such as phosphorus and organic matter content, and pH [START_REF] Séleck | Chemical soil factors influencing plant assemblages along copper-cobalt gradients: implications for conservation and restoration[END_REF][START_REF] Pourret | Assessment of soil metal distribution and environmental impact of mining in Katanga (Democratic Republic of Congo)[END_REF]. Each copper hill comprises several habitats, depending mainly on soil Cu and Co concentrations, as well as other soil chemical or physical properties, slope and microtopography [START_REF] Duvigneaud | Cuivre et Végétation au Katanga[END_REF][START_REF] Saad | Investigating the vegetation-soil relationships on the copper-cobalt rock outcrops of Katanga (DR Congo), an essential step in a biodiversity conservation plan[END_REF], Séleck et al 2013). This variation in MTE concentration in the soil, coupled with the extensive diversity of microhabitats can have a strong influence on the vegetation structure, allowing a large number of species to coexist on a single hill [START_REF] Saad | Investigating the vegetation-soil relationships on the copper-cobalt rock outcrops of Katanga (DR Congo), an essential step in a biodiversity conservation plan[END_REF][START_REF] Ilunga Wa Ilunga | Small-scale diversity of plant communities and distribution of species niches on a copper rock outcrop in Upper Katanga, DR Congo[END_REF], Faucon et al 2016). [START_REF] Delhaye | Community variation in plant traits along copper and cobalt gradients[END_REF] found clear patterns of variation in community weighted mean values of traits i.e. decreasing leaf area, increasing specific leaf area (SLA, the area of a leaf divided by its dry mass) and foliar MTE content along a gradient of increasing soil Cu. Most of this variation is due to species turn-over along the Cu gradient. In addition, a number of species occupy a very broad niche along the copper gradient and might exhibit significant intraspecific traits variation. However, Cu and Co availability to plants is probably highly variable between sites, depending on the local soil properties and the rhizosphere biogeochemical processes [START_REF] Lange | Prediction of the edaphic factors influence upon the copper and cobalt accumulation in two metallophytes using copper and cobalt speciation in soils[END_REF]. Species' functional response to metal concentration in the soil could therefore be highly site-specific. Recent works have found within species variation in Cu tolerance in this flora, correlated to soil copper content of native sites, suggesting local adaptation [START_REF] Faucon | Copper tolerance and accumulation in two cuprophytes of South Central Africa: Crepidorhopalon perennis and C. tenuis (Linderniaceae)[END_REF][START_REF] Peng | Differences in copper accumulation and copper stress between eight populations of Haumaniastrum katangense[END_REF][START_REF] Lange | Variation in copper and cobalt tolerance and accumulation among six populations of the facultative metallophyte Anisopappus chinensis (Asteraceae)[END_REF]. Therefore, copper hills represent an interesting model to explore trait variation within species along natural MTE gradients.

One of the species with the broadest ecological range on copper hills is Cyanotis longifolia Benth. (Commelinaceae). It is a very polymorphic, self compatible, hemicryptophytic species common in tropical Africa [START_REF] Owens | Self-incompatibility in the Commelinaceae[END_REF][START_REF] Faden | Flora of Tropical East Africa: Commelinaceae[END_REF]. C. longifolia is widespread on copper hills and was previously reported as a Cu and Co hyperaccumulator [START_REF] Malaisse | Copper and cobalt in vegetation of Fungurume, Shaba Province, Zaïre[END_REF]. In this paper, we explore the within species variation of functional traits in C. longifolia in relation to gradients of soil Cu concentration. Using populations growing on 5 copper hills, we examine the patterns of traits variation at the species level. Then, we examine the relative contribution of inter-and intra-population functional trait variation along MTE gradients. We test the hypothesis that such variation can be explained by within-and between sites differences in soil available copper content.

More precisely, we ask:

-How do C. longifolia functional traits vary along increasing copper concentration in the soil? We expect a change to traits related to a more stress tolerant strategy with increasing the copper content in the soil.

-What is the relative contribution of intra-versus inter-population variation to the species level variation? We expect the local copper gradient on each site to be the major source of variation in functional traits.

-Do all populations show similar patterns of trait variation along metal gradients? We expect that the soil copper gradient produces a strong filtering effect on functional traits resulting in similar patterns of traits variation between sites.

Material and methods

2.1.Study sites

Five sites were selected in the Copperbelt region in the south-east of D.R. Congo, between Tenke-Fungurume and Lubumbashi (Table 1). Each site was divided in 3 distinct habitats, based on topography and vegetation physiognomy, following [START_REF] Duvigneaud | Cuivre et Végétation au Katanga[END_REF], i.e. from base to top of hill: the dambo (high savannah on horizontal soil around the hill), mid-slope (grassland rich in geofrutex), and the upper slope, (open grassland rich in annual species). From the dambo to the upper part of the slope, soil MTE concentrations increase by two orders of magnitude [START_REF] Duvigneaud | Cuivre et Végétation au Katanga[END_REF], Séleck et al 2013). On each site, a population of C. longifolia is present and individuals grow from the bottom to the top of the hills, encompassing a large range of edaphic conditions.

2.2.Plant sampling and traits measurement

In January 2013, in each site, individuals of C. longifolia were collected along a transect ranging from the bottom to the top of the hill in order to capture the whole range of soil metal concentrations within each site. Population size of C. longifolia was small in several sites and habitats. Therefore, sample size did not exceed 10 percent of the individuals in each habitat, i.e. 22 to 65 replicates depending on site (Table 1). Sampled plants were at least 2 m from each other to avoid multiples samples of the same individual due to vegetative reproduction.

On each individual, 6 functional traits related to size (vegetative height: VH, leaf area: LA, rooting depth: RD), resource use and conservation (specific leaf area: SLA, leaf water content: LWC) and vegetative reproduction (clonal distance: CD) were measured. Leaves were collected for determination of concentrations of copper (Cu pl ) and cobalt (Co pl ). All morphological traits were measured according to Pérez-Harguindeguy et al ( 2013) on healthy adult plants with leaves exposed to full sunlight. VH was measured from the base of the plant to the top of the upper leaf. LA was measured on the two, fully developed, youngest undamaged leaves using ImageJ software on photographs of fresh leaves. LWC and SLA were measured on the same leaves after drying at 105°C for 48 hours. Leaf traits were averaged at the individual level. Recently, [START_REF] Vojtkó | Clonal vs leaf-height-seed (LHS) traits: which are filtered more strongly across habitats?[END_REF] emphasized the importance of clonal traits in response to habitat filtering. Therefore, the maximum CD was measured as the distance between the two farthest ramets of a same individual and the RD was measured after digging up carefully each individual.

Leaves used for SLA measurement were also used to determine MTE concentrations. Fresh leaves were washed in a 1% Alconox © solution, and rinsed [START_REF] Faucon | Revisiting copper and cobalt concentrations in supposed hyperaccumulators from SC Africa: influence of washing and metal concentrations in soil[END_REF]. The dry samples were ashed at 500°C for 12h. The ashes were diluted in 1 ml of H 2 O and 2 ml of HNO 3 65% [START_REF] Hoenig | Sample preparation steps for analysis by atomic spectroscopy methods: present status[END_REF] and heated at 100°C for 5 minutes. Each solution was filtered and diluted in H 2 O in order to obtain a 25 ml solution. Cu pl and Co pl were determined by inductively coupled plasma mass spectrometry (ICP-MS) (Thermo Scientific XSERIES2).

2.3.Soil analyses

A soil sample (approximately 50g dry weight) from the rhizosphere of each sampled plant was collected. Each sample was air dried for one week and sieved at 2 mm before analysis. Rock (gravel) content was assessed as the (1 -mass proportion) of the 2 mm sieved dried soil.

Soil pH was measured with a glass electrode on a 5:1 demineralised water:soil sample.

Organic matter content was measured by calcination for 12 hours at 550°C on 2 g soil samples that were previously heated at 105°C for 48 hours in order to remove any interstitial water [START_REF] Pansu | Handbook of soil analysis-Mineralogical, organic and inorganic methods[END_REF]. Mineral elements were extracted with 1M ammonium acetate-EDTA (pH 4.65) for 60 min using 5 g of dry soil in 25 ml of solution [START_REF] Cottenie | Chemical analysis of plants and soils. Instituut tot Aanmoediging van het Wetenschappelijk Onderzoek in Nijverheid en Landbouw[END_REF]. The supernatant was filtered and analysed by inductively coupled plasma-optical emission spectrometers (ICP-OES; Vista MPX, Varian Inc., Palo Alto, CA, USA) for the following elements: Cu, Co, Mn, Mg, P, K and Fe.

2.4.Statistical analysis

Soil parameters were log-transformed if necessary before analysis to meet normality assumptions. Variation of soil properties was explored with a principal component analysis.

Soil copper was highly correlated with the first principal component (see supplementary material 1, fig. S1). On copper hills, copper is known to have a strong filtering effect on vegetation structure [START_REF] Séleck | Chemical soil factors influencing plant assemblages along copper-cobalt gradients: implications for conservation and restoration[END_REF] and functional traits variation [START_REF] Delhaye | Community variation in plant traits along copper and cobalt gradients[END_REF].

For these reasons, soil copper was selected as the main gradient of interest in further analyses.

The association between soil Cu content and other soil parameters at each site were quantified using Pearson correlation coefficient (see also supplementary material 1).

Traits were log-transformed if necessary to meet the normality assumption. Within each site, the amplitude of variation of traits was evaluated using the coefficient of variation (CV) and trait min-max range. To test if large variability in soil conditions induces large variability in functional traits, Spearman correlations between traits CV or range and the range of soil Cu content at each site were used. Species level patterns of traits variation were evaluated by comparing the fit of a linear and a quadratic regression on the data, pooling the five populations. The best model was selected based on AICc criterion. When the difference in AICc < 2 [START_REF] Burnham | Multimodel inference: understanding AIC and BIC in model selection[END_REF]), the linear model was selected. To identify other potentially important soil factors, a redundancy analysis (RDA) was performed to quantify the multivariate variation of traits explained by all soil factors (see methods and results in supplementary material 1). Using this methodology, soil Cu was the factor explaining the most variation and was therefore further used as main gradient of interest.

To assess the relative contributions of intra-and inter-site variance to the total trait variance, a set of linear models were built. For each of the 8 traits, three different models were produced to extract the proportion of variance (R²) explained by the site effect and the local soil copper gradient at each site. The first model uses a simple linear regression where soil copper is the only explanatory variable (R² 1 ). This produces a single model forcing the same slope and intercept for all sites. The second model is a linear mixed model in which soil copper is the fixed effect and the site is a random effect (R² 2 ). This produces a regression line for each site with different intercepts but the same slope. Finally, the third is also a linear mixed model, with the soil copper content as a fixed variable and the interaction between the site and the soil copper concentration as a random variable (R² 3 ). The latter provides a random slope and random intercept model, allowing each site to have different slopes for trait variation along the copper gradient. The proportion of variance explained by the site is obtained by VarSite = R² 2 -R² 1 and the proportion of variance explained by the copper gradient within each site is obtained by: VarCu*Site = R² 3 -R² 2 . The residual variance is calculated by VarResidual = 1 -

(VarSite + VarCu*Site).
Linear regressions by permutation (n = 9999) were conducted for all traits along the copper gradients at each site separately in order to assess the functional response of each population to within-site environmental variation. To test if the different populations show similar patterns of trait variation along the copper gradients, the slopes of regression lines were compared between sites using a covariance analysis (ANCOVA) with a designed contrast matrix to compare slopes but not the intercepts in order to reduce the number of simultaneous tests. The same methodology was also applied with all other measured soil factors as a control and none showed more significant relationships than with soil copper content, suggesting that this is the main factor driving functional traits variation (results not shown). Analyses were conducted in R software 3.0.2 (www.R-project.org) using packages lme4, ade4, ape and multcomp, vegan and proxy.

Results

3.1.Soil

Soil Ac-EDTA extractible elements, stone and organic matter contents as well as pH, vary extensively both within and among sites (Table 1 and supplementary material 1, fig. S1a). The range of soil Cu concentration is shortest on Goma and longest on Fungurume. In addition to Cu, each site also shows extensive variation for other Ac-EDTA extractible elements, especially Co, Mn and Ca. Fungurume has the highest values in Cu, Co, Mn, Ca, Mg, P and pH. Goma is characterized by lower concentrations in Cu, Mn and organic matter. Kinsevere contrasts with all the other sites by its lower Co level (< 17 mg kg -1 ).

Table 1: Locations, number of plant and soil samples and variation range of soil parameters for each site. Rock is the soil gravel content (> 2 mm) and MO is the soil organic matter content, both expressed in percentage of soil mass. Ca, Co, Cu, Fe, K, Mg, Mn and P are expressed in mg kg -1 (Ac-EDTA extractable). In all sites except Kinsevere, soil Cu content is strongly positively correlated to Co content (Table 2). Cu is positively correlated with P and organic matter, significantly so in 4 of 5 sites (P) and 3 of 5 sites (organic matter). Other correlation patterns between Cu and other elements are highly site-specific (Table 2). On Fungurume, pH and Ca concentration increase with Cu concentration in the soil.

Goma

3.2.Plant traits

There is extensive variation both within and among populations for all traits (Fig. 1 and supplementary material 1, fig S1b). The population Goma has the largest range of size related traits and clonal distance (VH: 57 cm, LA: 122 mm², CD = 60 cm) and covers the whole range of trait values (Fig. 1). Fungurume has the highest foliar Cu and Co, up to 286 and 1700 mg kg -1 respectively. Using both traits CV and range (Supplementary material 2), there is no significant correlation between traits and soil Cu variability-e.g. the largest variability (CV and range) in size traits are found along the shortest soil Cu gradient (Goma).

3.3.Trait variation along the soil Cu concentration

The patterns of variation at the species level are trait specific (Fig. 2). VH, LA and RD show a highly significant quadratic relationship with soil Cu concentration with higher trait value at both ends of the gradient. Three traits show significant linear regression with soil Cu: SLA (negative slope), Cu pl and Co pl (positive slope). Leaf water content shows no significant relationship and clonal distance shows a negligible (R² = 0.04) quadratic relationship with soil Cu concentration. For size related traits (VH, LA and RD) the local soil Cu gradient explains between 28 and 51 % of the traits variation (Table 3). For SLA, LWC and CD, the site effect explains approximately twice more variance than the gradient effect. Interestingly, Cu pl and Co pl also show a larger site effect than the soil extractable-Cu effect (1.6 and 1.5 times respectively;

Table 3).

Table 3: Percentages of variance explained by the inter-site effect (Among site), the local soil copper gradient within each site (Cu within site) and the residuals expressed as a percentage of the total variance. Within each site, a linear regression analysis was conducted to examine the pattern of variation of each trait as a function of soil Cu. For morphological traits, the regression analysis and associated covariance analysis reveal highly contrasting, site specific patterns of trait variation along the Cu gradients (Fig. 3, supplementary material 3). Significant covariation with soil Cu was found in 1 to 4 sites depending on trait. Significant correlations in opposite directions were found in 4 of 8 traits (vegetative height, leaf area, rooting depth, leaf water content).

Discussion

Intraspecific trait variation is receiving increasing attention in plant community ecology [START_REF] Siefert | A global meta-analysis of the relative extent of intraspecific trait variation in plant communities[END_REF]. However, few studies have focused on traits variation at inter and intra population levels. Here, for the first time, the relative contribution of intra and interpopulation trait variation as well as patterns of traits variation in relation to MTE gradients are studied in a broad-niched metallophyte. Cyanotis longifolia is able to grow on soil with Ac-EDTA-extractable Cu concentration spanning more than two orders of magnitude (range: 30-7758 mg kg -1 ). The species occurs in plant communities that show dramatic variations in floristic composition and community averaged trait values, along copper gradients (Séleck et al 2013[START_REF] Delhaye | Community variation in plant traits along copper and cobalt gradients[END_REF] which are, at least partly, due to the increase in MTE concentrations in the soil.

Species level trait variation

Edaphic heterogeneity has significant impact on local intraspecific trait variation, especially in broad-niched species along altitudinal, fertility or aridity gradients [START_REF] Lenssen | Local adaptation of the clonal plant Ranunculus reptans to flooding along a small-scale gradient[END_REF][START_REF] Fajardo | Intraspecific trait variation and covariation in a widespread tree species (Nothofagus pumilio) in southern Chile[END_REF][START_REF] Pakeman | Intra-specific leaf trait variation: management and fertility matter more than the climate at continental scales[END_REF][START_REF] Harzé | Functional traits are more variable at the intra-than inter-population level: a study of four calcareous dry-grassland plant species[END_REF][START_REF] De Smedt | Habitat heterogeneity promotes intraspecific trait variability of shrub species in Australian granite inselbergs[END_REF]. In this study, functional traits of a broad niched species show large variation along short distance MTE gradients (Ac-EDTA extractable). With increasing soil Cu concentration, C. longifolia shows a decrease in SLA value concomitantly with an increase in tissues MTE concentrations. This relationship could be attributed to an increasing stress tolerance strategy of the plants with increasing the soil metal toxicity [START_REF] Potters | Stress-induced morphogenic responses: growing out of trouble?[END_REF]. SLA is related to growth rate and resource use efficiency [START_REF] Wright | The worldwide leaf economics spectrum[END_REF] and the increase of MTE concentrations in the plant tissues could induce a physiological stress while imposing an investment in metal sequestration mechanisms. The increase in MTE concentration in plant tissues is a well known feature of some species in the copper flora, especially in facultative metallophyte species [START_REF] Faucon | Revisiting copper and cobalt concentrations in supposed hyperaccumulators from SC Africa: influence of washing and metal concentrations in soil[END_REF][START_REF] Lange | Variation in copper and cobalt tolerance and accumulation among six populations of the facultative metallophyte Anisopappus chinensis (Asteraceae)[END_REF]. Most surprisingly, at the species level, the covariation of size traits with soil copper was quadratic with lowest values at intermediate soil Cu. The fact that several traits show similar patterns of response is not surprising, especially for traits related to the same functional axis, such as the plant size axis ( a et al 2016). Nonlinear traits responses along environmental gradients have been previously discussed (e.g.

Albert et al 2010) but the origin of these patterns often remains unclear. Here, contrasting site specific responses account for the quadratic response at the species level along the copper concentration gradient. It should be noted however that some traits might vary due to environmental factors independent of the soil MTE toxicity. For instance, size related traits show a negative correlation with the soil rock content (see RDA results in supplementary material 1). The increase in rock content in the soil could imply a lower water retention capacity in the soil, therefore selecting phenotypes adapted to water depletion. However, it was shown that rock content in the soil is not necessarily correlated to a decrease in soil water content in such environments [START_REF] Delhaye | Community variation in plant traits along copper and cobalt gradients[END_REF]. Further studies measuring other soil parameters on a larger number of sites, encompassing many different combinations of parameters, could provide further insight on the actual factors driving traits variation at the species level.

4.2.Within Population trait variation

Along gradients of environmental harshness and within a single species, large phenotypic variations and similar directions of trait variation are generally expected due to the strong restrictive effect of environmental conditions on plants phenotypes (e.g. [START_REF] Kolbas | Morphological and functional responses of a metal-tolerant sunflower mutant line to a copper-contaminated soil series[END_REF].

Here, an unsuspected complex pattern of trait variation within and among populations emerged. Intra population trait variations are highly site and trait specific along the studied copper gradients and account for variable proportions of total variance (between 5 and 51%) depending on trait. This is consistent with results found along altitude or resource gradients [START_REF] Wellstein | Intraspecific phenotypic variability of plant functional traits in contrasting mountain grasslands habitats[END_REF][START_REF] Lemke | Patterns of phenotypic trait variation in two temperate forest herbs along a broad climatic gradient[END_REF][START_REF] Harzé | Functional traits are more variable at the intra-than inter-population level: a study of four calcareous dry-grassland plant species[END_REF][START_REF] Pfennigwerth | Trait variation along elevation gradients in a dominant woody shrub is population-specific and driven by plasticity[END_REF]. Contrary to expectations, covariation with copper was less important compared to the site effect for most traits: only for size related traits was the impact of the local soil copper gradient superior to the site effect. This contradicts the results of a controlled experiment showing that most of the variation in morphological and physiological traits in another pseudometallophyte happened within population [START_REF] Meyer | Variability of zinc tolerance among and within populations of the pseudometallophyte species Arabidopsis halleri and possible role of directional selection[END_REF]. The large variation of size related traits along local gradients could indicate a trade-off between allocation to size and metal tolerance [START_REF] Ernst | Evolutionary biology of metal resistance in Silene vulgaris[END_REF]. The lower intra-population variation for traits related to resources use tends to show a strong conservatism of these traits along MTE gradients. This is consistent with previously reported results along resources or climatic gradients [START_REF] Fajardo | Intraspecific trait variation and covariation in a widespread tree species (Nothofagus pumilio) in southern Chile[END_REF][START_REF] Siefert | A global meta-analysis of the relative extent of intraspecific trait variation in plant communities[END_REF][START_REF] Harzé | Functional traits are more variable at the intra-than inter-population level: a study of four calcareous dry-grassland plant species[END_REF] but is also consistent with experimental findings on a facultative metallophyte from the copper hills, where SLA vary between populations but not in response to soil metal concentration [START_REF] Lange | Functional traits of a facultative metallophyte from tropical Africa: population variation and plasticity in response to cobalt[END_REF]. On the contrary, plants MTE contents exhibit large differences both within and between populations as found in other metallophytes [START_REF] Stein | Relationships between soil and leaf mineral composition are element-specific, environment-dependent and geographically structured in the emerging model Arabidopsis halleri[END_REF][START_REF] Frérot | Habitat heterogeneity in the pseudometallophyte Arabidopsis halleri and its structuring effect on natural variation of zinc and cadmium hyperaccumulation[END_REF] and show different patterns of covariation with other traits. The large part of the variance explained by the site effect for the plant Cu and Co content (24.6 and 32.9 % respectively) is likely due to the differences in soil metal concentrations as well as other factors that can influence soil MTE availability. On copper hills, Cu availability in the soil is strongly influenced by other edaphic factors, such as pH and organic matter content [START_REF] Lange | Prediction of the edaphic factors influence upon the copper and cobalt accumulation in two metallophytes using copper and cobalt speciation in soils[END_REF][START_REF] Pourret | Assessment of soil metal distribution and environmental impact of mining in Katanga (Democratic Republic of Congo)[END_REF]. This could play an important role on Fungurume and Kinsevere, where toxicity seems less clear on functional traits and organic matter content co-vary positively with soil Cu content. Soil organic matter content is known to immobilize Cu, leading to less free Cu ions in the soil solution [START_REF] Mcgrath | The effects of soil organic matter levels on soil solution concentrations and extractabilities of manganese, zinc and copper[END_REF]. Further, on Fungurume, the increase in soil pH and Ca content could be due to an increase in carbonates in the soil. Due to the high pH on the most contaminated soils on Fungurume, Cu could be immobilised in the soil (Kabata-Pendias 2010) and therefore represent a less toxic constraint for plants. However, other studies show that organic matter content and pH could not influence the proportion of free Cu 2+ content relative to the total Cu concentration in the soil [START_REF] Sauvé | Copper solubility and speciation of in situ contaminated soils: effects of copper level, pH and organic matter[END_REF]. On Fungurume, another study shows that a few plant species exhibit a clear decrease in size related traits and an increase in tissues Cu content [START_REF] Delhaye | Community variation in plant traits along copper and cobalt gradients[END_REF]. Population level differences in metal accumulation and tolerance were also demonstrated experimentally in several copper hill metallophytes (Faucon et Vellend 2010) or local adaptation [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF]). Short distance genetic differentiation was demonstrated along different types of environmental gradients such as altitude [START_REF] Byars | Local adaptation and cogradient selection in the alpine plant, Poa hiemata, along a narrow altitudinal gradient[END_REF][START_REF] Leempoel | Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata[END_REF] or in response to short distance variation in metal contamination [START_REF] Jain | Evolutionary divergence among adjacent plant populations I. The evidence and its theoretical analysis[END_REF][START_REF] Antonovics | Heavy metal tolerance in plants[END_REF]. Therefore, phenotypic plasticity could be the main mechanism of functional adjustment along short gradients (e.g. at Goma) while local adaptation could appear along large gradients in soil MTE concentrations (e.g. at Fungurume). This might explain the apparent distinct groups of individuals on soil with low and high metal content within the population of Fungurume (see supplementary material 1, Fig. S1). This hypothesis could be tested using genetic analysis and common garden experiments.

Traits covariation is also recognised as an important factor explaining the local diversity of phenotypes [START_REF] Armbruster | Causes of covariation of phenotypic traits among populations[END_REF]. Trade-offs between traits can produce different local phenotypes selected in particular environments [START_REF] Laughlin | Fitness of multidimensional phenotypes in dynamic adaptive landscapes[END_REF].

Finally, it is likely that other environmental factors such as mycorrhizal fungi or bacteria could improve plants MTE tolerance [START_REF] Lenoir | Arbuscular mycorrhi al fungal-assisted phytoremediation of soil contaminated with persistent organic pollutants: a review[END_REF][START_REF] Sun | Genetic diversity and characterization of heavy metal-resistant-endophytic bacteria from two copper-tolerant plant species on copper mine wasteland[END_REF] and are also influenced by local soil conditions [START_REF] Bauman | Multiscale assemblage of an ectomycorrhizal fungal community: the influence of host functional traits and soil properties in a 10-ha miombo forest[END_REF]. Therefore, we further advocate for experimental measurement of the variation in population level traits along Cu and/or Co gradients in controlled conditions [START_REF] Lange | Functional traits of a facultative metallophyte from tropical Africa: population variation and plasticity in response to cobalt[END_REF] in order to disentangle the relative proportion of phenotypic plasticity and ecotypic variation in C. longifolia.

Conclusion

Our study has revealed a complex pattern in the structure of intraspecific variation in a broad niched species. This suggests that generalization based on one or very few populations may be highly misleading due to the particular combination of local conditions at each site. A species should be sampled from sites encompassing its whole ecological range. Our results also stress that, depending on the spatial scale at which intraspecific variation is explored, very different patterns can emerge. This is likely due to interactions between isolation, various edaphic factors and/or local adaptation. A population specific approach is needed to capture the whole species level traits variation along toxicity gradient.

Fig. 1 :

 1 Fig. 1: Variation of eight traits for the five populations (G= Goma, O = Orthodoxe, Z = Zikule, K = Kinsevere, F = Fungurume). VH: vegetative height, LA: leaf area, RD: rooting depth, SLA: specific leaf area, LWC: leaf water content, CD: clonal distance, Cu pl and Co pl leaf Cu and Co content. The thick bar represents the median; the box contains the inter-quartile range (IQR) and the whiskers are the maximum 1.5 IQR.

Fig. 2 :

 2 Fig. 2: Trait variation at the species level, pooling the five studied populations, along the copper gradient. A linear and a quadratic model were fitted for each trait and the best model based on the AICc criterion was selected. The red line is the significant regression line. R² and associated p-value are reported. Colour code: Goma (green), Orthodoxe (red), Zikule (black), Kinsevere (Orange), Fungurume (blue).

Fig. 3 :

 3 Fig. 3: Functional traits variation along the copper gradient for the five studied sites. Plain lines represent significant linear regression, dashed lines are non-significant. Letters in the legend represent similarities in regression slopes of the different populations for each graph (see supplementary material 3 for exact values). Colour code: Goma (green), Orthodoxe (red), Zikule (black), Kinsevere (orange), Fungurume (blue).

  al 2012,[START_REF] Peng | Differences in copper accumulation and copper stress between eight populations of Haumaniastrum katangense[END_REF][START_REF] Lange | Variation in copper and cobalt tolerance and accumulation among six populations of the facultative metallophyte Anisopappus chinensis (Asteraceae)[END_REF] and might be driven by different MTE concentrations in the native soil, therefore suggesting genetic determinism of metal tolerance strategy at the population level. Therefore, the functional traits response to copper toxicity is likely speciesspecific as well as population-specific within species. Finally, the difference in the behaviour between both (Cu and Co) metals might be related to the greater mobility of Co at the plantsoil interface and the lack of efficient mechanism of Co detoxification(Lange et al 2017a).The absence of correlation between gradient length and functional traits variability in each population contradicts the hypothesis that larger niche implies larger trait variation[START_REF] Sandquist | Population-and family-level variation of brittlebush (Encelia farinosa, Asteraceae) pubescence: its relation to drought and implications for selection in variable environments[END_REF][START_REF] Sides | Revisiting arwin's hypothesis: oes greater intraspecific variability increase species' ecological breadth?[END_REF] but is consistent with observations along light availability gradients[START_REF] Lemke | Region and site conditions affect phenotypic trait variation in five forest herbs[END_REF]. This might be due to different, site specific, mechanisms of phenotypic variation on different isolated sites or on soils with different MTE concentrations within each site. It is not known if the covariations with soil Cu are due to phenotypic plasticity or local adaptation. Due to the geographical isolation, site specific responses of traits along local gradients may have evolved due to ecological drift (e.g.

Table 2 :

 2 Correlation coefficients between soil Cu concentration (Ac-EDTA extractable) and other soil factors at each site. Significant correlation coefficients (p<0.001) in bold.

		Co	Mn	Ca Fe	K	Mg	P	Rock MO pH
	Goma	0.95 0.07 0.09 -0.19 -0.28 -0.32 0.74 0.61 0.05 0.01
	Orthodoxe 0.89 0.84 0.08 0.11 0.43 0.05 0.96 0.50 0.81 -0.38
	Zikule	0.83 0.86 0.00 -0.46 -0.07 0.18 0.60 -0.24 0.30 0.39
	Kinsevere	-0.80 -0.42 0.67 0.11 0.25 0.33 0.97 0.67 0.80 -0.18
	Fungurume 0.96 -0.60 0.80 -0.71 0.29 0.24 0.99 0.57 0.63 0.70

Acknowledgments

GD is grateful to Kristel Wart and Alexandre Van Baekel for help with plant and soil analysis, to Gilles San Martin and David Bauman for useful statistical advice during data analysis and to Arnaud Jacobs for interesting discussions and comments on a draft.

Supplementary material 1: Multivariate analysis of the variability in plant traits and some soil characteristics and their multivariate correlations.

In order to investigate the extent of variation and differences in soil conditions and functional traits diversity in each sites, we performed 2 principal component analyses using the data presented in the material and methods (Figure S1). Differences between populations are tested using a multivariate analysis of variance by permutation (PERMANOVA). On the principal component analysis of plant traits (Fig S1a), the first three components explain 71% of the total variance. The fist PC is correlated to plant vegetative height, PC2 is correlated to leaf area and rooting depth and PC3 is correlated to specific leaf area and leaf cu content. Although intra-population variability is large, the different sites occupy different portions of the multivariate space (F=9.64, R²=0.21, p<0.001).

On the PCA of soil factors, the first component explains 41% of the variance and is correlated to soil Cu, P, Ca and Co content (Ac-EDTA extractable). The second component explains 22% of the total variance and is correlated to soil Ac-EDTA extractable Fe and Mg content (Fig S1b). The different populations also occupy different portions of the multivariate space (F=15, R²=0.30, p<0.001).

To assess the multivariate correlations between soil factors and functional traits, we used a redundancy analysis (RDA). The global model computed using all traits and all soil factors explains 34.5% of the total variance (adjusted R², Peres-Neto et al 2006) and is significant while tested with permutation of the residuals of the model [START_REF] Anderson | An empirical comparison of permutation methods for tests of partial regression coefficients in a linear model[END_REF]. The global model presents 4 significant axis (p perm <0.001). After removing the soil factors who showed too much multi-colinearity (Co and P), we selected the factors that contribute significantly to the RDA using a forward selection with two stopping criterions [START_REF] Blanchet | Forward selection of explanatory variables[END_REF]. Five edaphic factors were retained, and explain together 27.2 % of the variability. In order of decreasing contribution (adjusted R²): Cu, gravel content, Mn, Mg and Ca.

On figure S2, SLA is negatively correlated while Cu pl and Co pl are correlated positively with soil Cu content. However, soil Co and P content are also highly correlated with soil Cu content. Plant height, leaf area and rooting depth are negatively correlated with soil gravel content.

Figure S2: plot of the redundancy analysis on the 8 measured traits and the 5 significant soil parameters explaining 27% of the variation in functional traits. Each point represents an individual. Functional traits are represented by the red vectors and soil parameters by the blue vectors. VH: vegetative height, LA: leaf area, RD: rooting depth, SLA: specific leaf area, LWC: leaf water content, CD: clonal distance, Cu pl : leaf copper content, Co pl : leaf cobalt content, Rock: soil gravel content.

Supplementary material S2:

Regression tables for the 6 morphological and 6 chemical traits along the soil copper gradient.


Jacques Ophoff
email: jacques.ophoff@uct.ac.za

Frauke Dietz

Using Gamification to Improve Information Security Behavior: A Password Strength Experiment

Keywords: Information Security Behavior, Gamification, Authentication, Password Strength Feedback

Information security emphasizes the importance of motivating end users to improve their security behavior towards protecting their private and organizational information assets. Password authentication is widely used as a user authentication method to safeguard information resources from unauthorized access. Despite its prevalence password best practice is not often followed and the use of weak passwords persist. Although password strength feedback mechanisms commonly aim to extrinsically motivate users to improve their password creating behavior, it is not yet clear how other methods, specifically gamification, influences security behavior regarding password creation behavior. The purpose of this study is to examine the effect gamification on user information security behavior, specifically regarding password creation. This study presents results from an online experiment of 232 respondents, who interacted with two different password strength feedback methods, namely a meter feedback method and a gamified feedback method using gamification points. A significant difference between the methods was found when measuring password strength using the number of guesses needed to crack the password, with the points method resulting in stronger passwords. The results of the study reveal that gamified feedback can lead to increased engagement and stronger password creation.

Introduction

A common approach used to protect user's information assets from unauthorized access, is using user authentication methods [START_REF] O'gorman | Comparing passwords, tokens, and biometrics for user authentication[END_REF]. A popular form of authentication that is used in information security is password verification and hence, it is especially important to motivate users to improve their security behavior regarding password creation [START_REF] Mwagwabi | Improving Compliance with Password Guidelines: How User Perceptions of Passwords and Security Threats Affect Compliance with Guidelines[END_REF][START_REF] Van Acker | Password Meters and Generators on the Web: From Large-Scale Empirical Study to Getting It Right[END_REF][START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF]. To motivate users to improve their security behavior regarding password creation, users are often exposed to password strength feedback mechanisms, such as password meters, that commonly extrinsically motivate users to create stronger passwords through fear [START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF][START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF][START_REF] Kim | YourPassword: Applying Feedback Loops to Improve Security Behavior of Managing Multiple Passwords[END_REF][START_REF] Ur | How Does Your Password Measure Up? The Effect of Strength Meters on Password Creation[END_REF]. Considering human motivation, intrinsic motivation is increasingly found to be more effective than extrinsic motivation [START_REF] Bénabou | Intrinsic and Extrinsic Motivation[END_REF][START_REF] Lowry | Taking "Fun and Games" Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM)[END_REF], hence increasing the interest in investigating the effects of intrinsic motivation on user information security behavior. Within the context of information security behavior, the effects of gamification on user information security behavior, specifically regarding password creation behavior, are still undiscovered. This study contributes to information security behavior research by investigating this phenomenon. The main objective of this study is to examine the effect of gamified feedback on user information security behavior, specifically regarding password creation. Thus, the primary research question to be addressed in this study is: How does gamified feedback affect user information security behavior regarding password creation? This study further aims to investigate an alternative password strength feedback method to existing fear-driven methods (password meter), to potentially motivate users more effectively to create strong passwords. The research question is evaluated using empirical data, which is collected using an online experiment.

The remainder of this paper will proceed as follows. First, the conceptual background relating to information security behavior around password creation and the adopted research model will be presented, along with research propositions for this study. In Section 3, the research design will be discussed in detail. This is followed by the data analysis and discussion of the project findings. Finally, this paper concludes by discussing the limitations of this study, along with opportunities for future research.

Literature Review

Password verification is the most commonly used form of authentication in information security [START_REF] Mwagwabi | Improving Compliance with Password Guidelines: How User Perceptions of Passwords and Security Threats Affect Compliance with Guidelines[END_REF][START_REF] Van Acker | Password Meters and Generators on the Web: From Large-Scale Empirical Study to Getting It Right[END_REF][START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF]. Password authentication is a user authentication method that protects valuable information assets and resources within computer-based systems from unauthorized access and violation [START_REF] Furnell | Essential Lessons Still Not Learned? Examining the Password Practices of End-Users and Service Providers[END_REF] by "matching the combination of username and password against credentials stored on the server" [START_REF] Van Acker | Password Meters and Generators on the Web: From Large-Scale Empirical Study to Getting It Right[END_REF]. Although passwords form an important barrier limiting unauthorized access to information assets, users still engage in poor password practices, such as creating insecure passwords and reusing passwords [START_REF] Mwagwabi | Improving Compliance with Password Guidelines: How User Perceptions of Passwords and Security Threats Affect Compliance with Guidelines[END_REF]. Mwagwabi et al. [START_REF] Mwagwabi | Improving Compliance with Password Guidelines: How User Perceptions of Passwords and Security Threats Affect Compliance with Guidelines[END_REF] argued that poor password practices amongst users occur because users find it difficult to remember passwords and consider it annoying to have to comply with inconsistent password guidelines. Using weak passwords increases the user's vulnerability to threat, as weak passwords can be cracked without difficulty by easily accessible password cracking software [START_REF] Van Acker | Password Meters and Generators on the Web: From Large-Scale Empirical Study to Getting It Right[END_REF][START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF]. Given the threats to password security, it is especially important to find ways to improve the security behavior of users by motivating end users to create strong and effective passwords [START_REF] Mwagwabi | Improving Compliance with Password Guidelines: How User Perceptions of Passwords and Security Threats Affect Compliance with Guidelines[END_REF][START_REF] Van Acker | Password Meters and Generators on the Web: From Large-Scale Empirical Study to Getting It Right[END_REF][START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF].

Extrinsic and Intrinsic Motivation

Fear appeals, and theories related to fear, including the protection motivation theory and deterrence theory, are extensively used in the field of information security to assist in motivating end users to change their security behavior [START_REF] Boss | What Do Systems Users Have to Fear? Using Fear Appeals to Engender Threats and Fear That Motivate Protective Security Behaviors[END_REF][START_REF] D'arcy | A review and analysis of deterrence theory in the IS security literature: making sense of the disparate findings[END_REF][START_REF] Johnston | Fear Appeals and Information Security Behaviors: An Empirical Study[END_REF]. Fear appeals present end users with persuasive messages that warn the end user of potential threats and recommend security behavior to counteract the threat [START_REF] Boss | What Do Systems Users Have to Fear? Using Fear Appeals to Engender Threats and Fear That Motivate Protective Security Behaviors[END_REF]. Fear appeals refer to persuasive messages designed to highlight "the seriousness of a threat and a user's ability to cope with it" [START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF]. Johnston and Warkentin [START_REF] Johnston | Fear Appeals and Information Security Behaviors: An Empirical Study[END_REF] defined fear appeals as messages that aim to encourage individuals to act in a recommended way through the initiation of fear related to a threat. Fear appeals act as an extrinsic motivator for human behavior, as these propel users externally into choosing stronger passwords. Extrinsic motivation refers to human behavior that is driven by external rewards [START_REF] Gagné | Self-determination theory and work motivation[END_REF][START_REF] Ryan | Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions[END_REF]. Ryan and Deci [START_REF] Ryan | Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions[END_REF] further pointed out that, if individuals are externally pushed into performing an action, the motivation behind performing that action is extrinsic. When taking human motivation into consideration, intrinsic motivation has sometimes been found to be more effective regarding human motivation than extrinsic incentives [START_REF] Bénabou | Intrinsic and Extrinsic Motivation[END_REF][START_REF] Lowry | Taking "Fun and Games" Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM)[END_REF]. Intrinsic motivation refers to behavior that is driven by personal interest and enjoyment in doing something [START_REF] Gagné | Self-determination theory and work motivation[END_REF][START_REF] Ryan | Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions[END_REF]. The idea of intrinsic motivation is also reflected in the self-determination theory (SDT), which focuses on the degree to which an individual's behavior is self-motivated without external rewards [START_REF] Deci | Handbook of self-determination research[END_REF].

Password Meters as Password Strength Feedback Mechanisms

A common existing measure for motivating users to create stronger passwords is the use of password meters as a real-time password strength feedback mechanism to communicate a password's strength visually as the password is typed [START_REF] Vance | Enhancing Password Security through Interactive Fear Appeals: A Web-Based Field Experiment[END_REF][START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF][START_REF] Kim | YourPassword: Applying Feedback Loops to Improve Security Behavior of Managing Multiple Passwords[END_REF][START_REF] Ur | How Does Your Password Measure Up? The Effect of Strength Meters on Password Creation[END_REF]. Feedback mechanisms, also referred to as feedback loops, "provide people with timely information about their actions and opportunities to improve them", hence pushing people towards improved behaviors [START_REF] Kim | YourPassword: Applying Feedback Loops to Improve Security Behavior of Managing Multiple Passwords[END_REF]. Password meters generally appear as a colored bar that changes its color and its length based on the strength of the password entered, and it is often accompanied by a word explicitly representing the strength (e.g., weak, medium or strong) of the password [START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF]. An example of a password meter, which is displayed during account registration, is depicted in Fig. 1.

The strength and quality of a given password is commonly measured by either enforcing strong password requirements around password length and character set complexity, or by detecting "weak patterns such as common words, repetitions and easy keyboard sequences" [START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF]. Past studies conducted around the use of password meters concluded that password meters as a password strength feedback mechanism, in general, effectively influence users' security behavior towards choosing stronger passwords [START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF][START_REF] Ur | How Does Your Password Measure Up? The Effect of Strength Meters on Password Creation[END_REF][START_REF] Egelman | Does My Password Go Up to Eleven?: The Impact of Password Meters on Password Selection[END_REF]. However, Ur et al. [START_REF] Ur | How Does Your Password Measure Up? The Effect of Strength Meters on Password Creation[END_REF] observed that password meters with too-strict evaluations had a negative impact on users, resulting in users getting irritated and therefore losing the motivation to satisfy the meter.

Password meters persuade users to create stronger passwords through fear by warning the users that the chosen password is weak or not strong enough. However, it has been found that methods using fear appeal are not necessarily the most effective in achieving security behavior by users [START_REF] D'arcy | A review and analysis of deterrence theory in the IS security literature: making sense of the disparate findings[END_REF]. D'Arcy and Herath [START_REF] D'arcy | A review and analysis of deterrence theory in the IS security literature: making sense of the disparate findings[END_REF] argued that the increased deterrence through fear appeals does not work for all users. It is, therefore, of increasing interest to research and investigate alternative password feedback mechanisms that will motivate users more effectively to create stronger passwords.

Gamification as an Opportunity to Improve Password Security Behavior

Deterding [START_REF] Deterding | The Lens of Intrinsic Skill Atoms: A Method for Gameful Design[END_REF] argued that the SDT framework models and explains an individual's gaming motivation and enjoyment very accurately. Over the last five years, gamification has become an emerging trend and has gained increasing popularity within the academic context regarding intrinsically motivating an individual's behavior [START_REF] Hamari | Does Gamification Work? -A Literature Review of Empirical Studies on Gamification[END_REF]. Making use of gamification in the security context, specifically regarding authentication, could increase the motivation of users to act more securely [START_REF] Kroeze | Gamifying authentication[END_REF][START_REF] Ophoff | Examining Gamification as a Driver of Individual Information Security Behavior[END_REF].

Gamification can be briefly defined as "the use of game design elements in nongame contexts to motivate and increase user activity" [START_REF] Deterding | From Game Design Elements to Gamefulness: Defining "Gamification[END_REF]. To emphasize the behavioral effects of gamification, an alternative definition of gamification presented by Hamari et al. [START_REF] Hamari | Does Gamification Work? -A Literature Review of Empirical Studies on Gamification[END_REF] is acknowledged as "a process of enhancing services with (motivational) affordances to invoke gameful experiences and further behavioral outcomes." Recently, human-computer interaction research has become increasingly interested in the idea of utilizing game design for designing interactive systems for motivation and enjoyment [e.g. [START_REF] Deterding | The Lens of Intrinsic Skill Atoms: A Method for Gameful Design[END_REF][START_REF] Jordan | Designing Pleasurable Products: An Introduction to the New Human Factors[END_REF][START_REF] Zhang | Technical Opinion: Motivational Affordances: Reasons for ICT Design and Use[END_REF].

Blohm and Leimeister [START_REF] Blohm | Gamification -Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change[END_REF] argued that game design elements can be utilized within processes and services to influence users' motivation, productivity and behavior positively. Game design elements refer to motivational affordances that drive psychological and behavioral outcomes of users [START_REF] Hamari | Does Gamification Work? -A Literature Review of Empirical Studies on Gamification[END_REF].

Game design elements consist of game mechanics and game dynamics. Game mechanics are functional components used to gamify processes or applications. Game dynamics, on the other hand, determine the effects of game mechanics on a user's long-term user experience of the implemented mechanics. These game dynamics correspond to specific user motives that drive specific user behavior [START_REF] Blohm | Gamification -Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change[END_REF][START_REF] Thiebes | Gamifying Information Systems -A Synthesis of Gamification Mechanics and Dynamics[END_REF]. For example, game mechanics such as scoring systems (i.e. gamification points) induce collective dynamics that relate user activities to measurable improvements and thus satisfy the aspiration for achievement. Blohm and Leimeister [START_REF] Blohm | Gamification -Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change[END_REF] further argued that, depending on the specific design of a mechanic, a specific mechanic may induce various dynamics and hence may lead to different motives.

Past studies conducted around improving the impact of password meters as a feedback mechanism for password strength, focused solely on variation in scoring algorithms and visual representations of password meters [START_REF] Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF][START_REF] Ur | How Does Your Password Measure Up? The Effect of Strength Meters on Password Creation[END_REF][START_REF] Egelman | Does My Password Go Up to Eleven?: The Impact of Password Meters on Password Selection[END_REF]. Shay et al. [START_REF] Shay | A Spoonful of Sugar?: The Impact of Guidance and Feedback on Password-Creation Behavior[END_REF] pointed out that "past research has not looked at the impact of presentation and instructions (beyond password meters), or at ways to help users cope with strict [password] requirements." After conducting literature searches relating to this literature review, it has been found that no research has focused on the use of gamification within the context of password feedback mechanisms. This gap in the literature, therefore, offers an opportunity to research whether password feedback mechanisms that make use of gamification could potentially enhance motivation towards better password choice. Specifically making use of gamification points for password strength feedback is of great interest, as a point system is the game mechanic that can be considered most like the percentage calculation used by the password meter approach. The theoretical proposition in this study therefore is that the use of gamification points, as opposed to conventional password meters, as a password feedback mechanism will result in users creating stronger passwords.

Methodology

This study used a true experiment, as participants were randomly assigned to treatment groups, namely the experimental group and the control group [START_REF] Creswell | Research Design: Qualitative, Quantitative and Mixed Methods Approaches[END_REF]. More specifically, a Posttest-Only Control-Group Experimental Design was utilized, as the treatment groups were measured only on the posttest provided after the experiment. The experimental group (Group A) were given a treatment or planned intervention, while the control group (Group B) were not given a treatment. For this study, the experimental group were given a point feedback method as treatment, while the control group were presented with the standard password meter. Both treatment groups were measured on the posttest only, as the study aimed to investigate whether user security behavior differs based on different feedback methods. Treatment groups were not administered a pretest, as the study was not focusing on the change in security behavior before and after a specific treatment.

To add realism to the experiment, the experiment was designed to look like a standard sign-up form to create a new online account. Participants were also presented with pre-experiment instructions that emphasized that participants should fill in the sign-up form as they would in a normal sign-up process for an account that holds sensitive personal information. However, it is acknowledged that participants cannot be forced to treat the sign-up process in the experiment the same way they would treat a sign-up process in a real-world situation.

Sampling

For this study a non-probability convenience sample was selected, consisting of students and staff at a large South African university, as these individuals were readily available and could be reached via email. The sample is believed to be a fair representation of the target population (general users of information systems) -the sample uses several information systems and includes individuals of different ages and genders, who have different levels of technical skills and password creating experience. All individuals in the sample operate within a tertiary institution, which requires them to create passwords for numerous electronic applications, often containing sensitive personal information.

Data Collection Method

An online experiment, followed by an online questionnaire, was conducted to gather primary data for this research. An email, briefly explaining the purpose of the study and providing a link to the online experiment, was sent out to the individuals in the identified sample. Potential participants were provided only with a brief description of the study (investigation into information security behavior), as informing participants that the study was investigating information security behavior specifically around password strength might have influenced their password-creating behavior. Telling participants about the purpose of the study may lead to demand characteristics, which results in participants interpreting the purpose of the experiment and subconsciously behaving differently to meet their interpretation [START_REF] Orne | Demand Characteristics and the Concept of Quasi-Controls 1[END_REF].

The experiment consisted of two different simple account registration interfaces, each representing a different password strength feedback mechanism. One registration interface represented the conventional password meter feedback method (see Fig. 2), while the other registration interface represented a feedback method that used gamification points (see Fig. 3). Each participant was randomly presented with one of the interfaces and was asked to complete the registration process, which required the user to create a password. Only the password and the confirmation of password were compulsory. These two fields did not have to match to proceed to the questionnaire, however it was recorded whether they matched or not (94% of participants entered matching passwords). The password strength feedback mechanism was updated in real time as a participant entered a password. The real-time calculated password strength score was stored as primary data, avoiding the permanent storage of the actual password created. The password strength was calculated using the zxcvbn library developed by Dropbox. Zxcvbn is a password strength estimator, which recognizes and weighs "several dictionaries (English words, names and surnames, Burnett's 10,000 common passwords), spatial keyboard patterns (QWERTY, Dvorak, and keypad patterns), repeats (aaa), sequences (123, gfedcba), years from 1900 to 2019, and dates (3-13-1997, 13.3.1997, 1331997)" through pattern matching and conservative estimation [START_REF] Wheeler | zxcvbn: realistic password strength estimation[END_REF].

The zxcvbn library awards the password strength with a score of 0 (too guessable/very weak), 1 (very guessable/weak), 2 (somewhat guessable/moderate), 3 (safely not guessable/strong) or 4 (strongly not guessable/very strong) [START_REF] Wheeler | zxcvbn: realistic password strength estimation[END_REF]. The zxcvbn library was also utilized to calculate additional properties (other than the password score) of a participant's password, including the estimated number of guesses needed to crack the password, the estimated time (in seconds) to crack the password, and the number of milliseconds it took zxcvbn to calculate the password score. These properties were all calculated in real time during password creation and were stored after questionnaire completion. The conventional strength meter (the color and the length of the bar) was populated based on the calculated password score.

For the feedback method that used gamification points, the guesses_log10 password property was used to calculate and update the total point score. The guesses_log10 is the estimated guesses needed to crack a password, expressed as base-10 logarithm [START_REF] Wheeler | zxcvbn: realistic password strength estimation[END_REF], thus yielding a user-friendly indication of password strength. For each character that was added or removed from the password during password creation, the difference between the old and new guesses_log10 value was calculated, multiplied by one hundred and displayed as the adjustment, while the total score was adjusted accordingly (see Fig. 4). The example in Fig. 4 shows how the password score could be negatively affected by using a dictionary word: going from 'universit' to 'university' decreases the password score by 347 points. Unlike the strength meter, the points feedback method does not have a maximum password strength limit, as the guesses_log10 password property can still increase further even if a password has a strength score of 4 (100%) already. This enabled users to score an infinite amount of points.

Qualtrics, a web-based survey service, was used to host the online experiment, create the online questionnaire, and collect and record the response data in the cloud. Response data refers to data that the respondents provide by participating in the experiment and answering the questionnaire.

Data Analysis and Findings

A total of 445 responses were recorded. Of this 232 (52%) were considered valid for analysis due to fully completing the entire experiment. Out of the valid responses, 112 (48%) respondents were presented with the meter feedback method and 120 respondents were presented with the points feedback method. Most respondents were female (56%). Age groups were divided into seven categories. Most respondents (45%) were 18-24 years old, containing mostly students at UCT. Twenty-one per cent of respondents fell within the 35-44 age group, closely followed by 19% in the age group 25-34 years and 12% in the age group 45-54 years. Only 4% of respondents fell within the age group 55-64 years. Respondents were asked whether they play any form of digital, computer or video games. A large number (59%) of respondents play games, while 41% do not play any form of digital, computer or video games.

Assessment of the Average Password Strength

Both password strength feedback methods assigned the password strength with a score of 0, 1, 2, 3 or 4 (0 representing very weak and 4 representing very strong). A t-Test for independent means was performed to test whether there was a significant difference in the average password strength between the two treatment groups, which were independent of one another. The two treatment groups are referred to as being independent, as these groups were unrelated, and each participant was tested only once. IBM SPSS Statistics 25 was used to perform an independent-samples t-Test. The summary results of the t-Test are shown in Table 1. The results of the analysis show that, although Group A (points feedback method) did have a slightly higher average password score than Group B (meter feedback method), that score was not significantly different. However, when examining password strength in terms of estimated guesses needed to crack the password (guesses_log10) a significant difference was observed, with Group A having more secure passwords.

In addition to the password score, other properties relating to each feedback method were recorded or calculated. One such property is the time spent on the sign-up form, while another is the time taken to calculate the password score (a higher time indicates a more complex password). Averages scores for these metrics, across all participants, are shown in Table 2. Respondents who were presented with the points feedback method, on average, spent more time on the simulated sign-up process than did respondents who were presented with the meter feedback method. This time, also referred to as dwell time, can be seen as a possible proxy measure for engagement [START_REF] O'brien | A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form[END_REF]. The average time it took zxcvbn to calculate the password score (like the average time to crack a password) was higher for passwords created by respondents who received the points feedback method. This corresponds with the slightly higher average password score attained by respondents who were presented with the points feedback method on the sign-up page.

The findings therefore partially (except for the use of score for calculation of password strength) support the proposition that the use of gamification points, as opposed to conventional password meters, as password feedback mechanism will result in users creating stronger passwords.

Discussion

Although the difference in password score was not statistically significant, the average password score and averages of other password properties were found to be higher for passwords created with the points feedback method and therefore revealed a slight increase in password strength for passwords created with the gamification feedback method. Interestingly, the findings of this study revealed that, on average, respondents who were presented with the points feedback method spent more time completing the sign-up page than respondents who were presented with the meter feedback method. This could be explained by arguing that the respondents who interacted with the points feedback method might have been more engaged in creating a password, when compared with the respondents, who, on average, spent less time interacting with the meter feedback method.

The findings of this study further revealed that the average time to crack a password was significantly higher for passwords created by respondents who received the points feedback method. This could be explained by the fact that the crack time property of a password can still increase further even if a password has a strength score of 4 already. Since the points feedback method did not visually indicate an upper strength limit (i.e. password score of 4 represented as "very strong"), when compared with the conventional meter feedback method, users may have been encouraged to continue to improve the strength of their password (in terms of crack time) even if their password achieved the highest attainable password score of 4 already.

The implications of the above are twofold. First it shows potential for gamification to enhance user engagement in password creation, possibly resulting in stronger passwords. Second it points to a deficiency in password strength meters in that once a maximum (or good enough) score is reached users likely stop. Switching to a strength indication with no upper bound (e.g. estimated guesses needed to crack a password) would likely result in stronger passwords.

Conclusion

Information security is becoming increasingly important owing to increasing globalization and computing complexity and hence, it is becoming more and more important to motivate end users to improve their security behavior towards safeguarding their information assets and resources. This study contributed an alternative password strength feedback method to existing fear-driven methods (password meters), which used gamification points, and further investigated this password strength feedback method using empirical data.

The results of this study revealed that gamification points as a password strength feedback method did not significantly influence users' security behavior more positively than the meter feedback method, as no statistically significant difference regarding password strength and intrinsic motivation towards better password choice was found between the two password strength feedback methods. However, considering the average password score and averages of other password properties (e.g. the time to crack a password), the findings still revealed a slight increase in password strength for passwords created by respondents who were presented with the points feedback method.

This study is limited by the sample and context. The data, therefore, should be interpreted in this context and cannot be generalized to all internet users. Furthermore, this study is limited by the experimental environment, which cannot force respondents to treat the sign-up process as seriously as they would in a real-world situation. This study is also not able to reveal whether password strength was influenced by the password strength feedback method or by other factors (e.g. user's personality, curiosity or password perception). It is acknowledged that potential participants were provided with a brief description of the study ('investigation into information security behavior') which may lead to demand characteristics, resulting in participants interpreting the purpose of the experiment and subconsciously behaving differently to meet their interpretation. We tried to minimize this possibility by not disclosing our focus on password strength and password-creating behavior.

Future research could expand the experiment with questions that investigate whether respondents acknowledged and interacted with the feedback method or not. Password strength should also be investigated further through the consideration and testing of passwords' estimated crack times to determine password strength. The estimated crack time of a password has no upper limit, unlike the strength score of a password, and could serve as a more accurate measure of password strength.

Fig. 1 .

 1 Fig. 1. Password meter example (https://lastpass.com/)

Fig. 2 .Fig. 3 .

 23 Fig. 2. Registration form with a conventional password strength meter

Fig. 4 .

 4 Fig. 4. Password strength points feedback concept

Table 1 .

 1 Password strength t-Test data summary

	Strength calculation Score (0-4)	guesses_log10
	Feedback method	Meter Points Meter Points
	Mean	2.85	2.87	9.19	10.51
	Std. Deviation	1.21	1.23	3.69	6.29
	Observations	112	120	112	120
	Degrees of freedom 230		230
	t statistic	-.115		-1.929
	p value	.461		.010

Table 2 .

 2 Additional analysis of experiment and passwords

	Meter Points

Acknowledgements. This work is based on the research supported wholly / in part by the National Research Foundation of South Africa (Grant Numbers 114838).