# Factors Affecting the Presence and the Diversity of Bryophytes in the Petrifying Sources Habitat (7220) in Wallonia and the Brussels-Capital Region, Belgium

Authors: J.-M. Couvreur; G. San Martin; A. Sotiaux
Journal: International Journal of Agronomy
 (2016)

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## Abstract

Bryological composition, water chemistry, and environmental factors were characterized on 67 Belgian travertines. We explore the relationship between these environmental factors and the community composition, species richness, or presence of individual species using Redundancy Analysis with Hellinger’s transformation (tb-RDA) or Generalized Linear Models (GLMs). The best variables explaining the community composition are slope, NO3, NH4, and PO4. The species richness is negatively related to canopy cover and PO4.Palustriella commutata tends to be more frequent when the slope is steeper and to a lesser degree when the canopy cover is lower.Eucladium verticillatum tends to be slightly more frequent when canopy cover and NH4 concentrations are lower.Cratoneuron filicinum is more frequent at higher Mg concentrations andPellia endiviifolia is more frequent at lower PO4 concentrations and higher NO3 concentrations.Brachythecium rivulare showed wide ecological amplitude and almost none of the tested environmental factors seem to be related to its presence. The study identifies eutrophication as the main factor responsible for habitat deterioration. Practical indications on the best ways to maintain or to enhance the quality of these petrifying sources are given.


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## Body

## 1. Introduction

“Travertine” or “tufa” deposits include a wide variety of calcareous substrates that are characterized not only by their mineral composition but also by their morphology and processes by which they form. There are many interpretations and definitions of the terms tufa and travertine (Symoens et al. [1], Couderc [2], Pentecost [3], De Zuttere [4], Viles and Goudie [5], Ford and Pedley [6], Janssen and Swennen [7], Merz-Preiß and Riding [8], Boch et al. [9], Franco et al. [10], and Brusa and Cerabolini [11]). The term “travertine” is often used to describe calcareous substrates formed during warm periods or in warm environment including thermal springs and that contain no or very few organic material other than bacteria [6, 7]. In contrast, the term “tufa” is generally used to describe substrates formed in colder environments and containing organic material [6]. It is possible for both warm and cold travertine formation to occur simultaneously, as evidenced by several Belgian locations including the Hoyoux river near Huy and on the “Ry de Matignolles” stream near Treignes [10].Although the European definition of the habitat [12] uses the term “tufa” we will use the term “travertine” in accordance with Pentecost [13] as the term “tufa” relates more to a soft and poorly consolidated variety of travertine.In the framework of the mapping of Natura 2000 sites in Wallonia, south Belgium, we had the opportunity to undertake research on the ecology of the priority habitat “Petrifying springs with tufa formation (Cratoneurion) 7220.” The code 7220 is specific to this priority habitat in the Interpretation Manual of European Union Habitats [12]. This priority habitat generally consists of small point or linear formations dominated by bryophytes (Cratoneurion commutati Koch, 1928, communities). The manual [12] provides a list of characteristic species for each habitat type; however, these lists have to be adapted at the national or regional scale. In Wallonia, two bryophyte species are considered strictly linked to the 7220 habitat,Palustriella commutata (Hedw.) Ochyra andEucladium verticillatum (With.) Bruch & Schimp.Palustriella commutata (Hedw.) Ochyra has been split into two species by some authors [14]; however, in this study we consider both asPalustriella commutata.The following are considered to be good companion species and are also included in the Walloon definition when assessing the habitat conservation status [15]:Aneura pinguis (L.) Dumort.,Conocephalum conicum (L.) Dumort.,Jungermannia atrovirens Dumort.,Leiocolea badensis (Gottsche.) Jörg.,Pellia endiviifolia (Dicks.) Dumort.,Preissia quadrata (Scop.) Nees,Brachythecium rivulare Schimp.,Bryum pseudotriquetrum (Hedw.) P. Gaertn. et al.,Ctenidium molluscum (Hedw.) Mitt.,Cratoneuron filicinum (Hedw.) Spruce,Dichodontium pellucidum (Hedw.) Schimp.,Didymodon tophaceus (Brid.) Lisa,Fissidens adianthoides Hedw.,Fissidens crassipes Wilson ex Bruch & Schimp.,Gymnostomum calcareum Nees & Hornsch.,Hymenostylium recurvirostrum (Hedw.) Dixon,Philonotis calcarea (Bruch & Schimp.) Schimp.,Plagiomnium ellipticum (Brid.) T. J. Kop.,Plagiomnium rostratum (Schrad.) T. J. Kop.,Plagiomnium undulatum (Hedw.) T. J. Kop., andSeligeria trifaria (Brid.) Lindb.Cratoneuron filicinum is considered a good characteristic species in other regions and countries, for example, Flanders and the Brussels-Capital Region [16], Great Britain [17], and the Netherlands [18]. However, it was not included in the small group of characteristic species as it is far more widespread in Wallonia than the two other species [19], occurring in a variety of different habitats, including man-made calcareous forest tracks. NonethelessCratoneuron filicinum is considered to be a good companion species for the 7220 habitat in Wallonia [15].In Wallonia [15], the 7220 habitat is mainly associated with calcareous springs and small streams (width < 5 m) where incrustation processes occur. An atypical form of the habitat is represented in Wallonia by the “travertins” formed in the Hoyoux river consisting of calcareous deposits across the river (barrages) or its tributaries whose width can reach 10 to 20 m. The 7220∗ habitat in Wallonia [15] is characterized by Ca-rich waters (110–120 mg·L−1) and high pH values (7.5–8.5) but depending on the subregion the substrate can be mostly sandy with calcareous incrustations (sandy-loam region) or more compact calcareous rocks (Condroz, Famenne-Calestienne, Lorraine) where they are locally called “crons” or “crânières” [1, 20–24].From a phytosociological point of view, most of the springs and small streams of Wallonia and Brussels-Capital Region can be attributed to theMontio-fontanae-Cardaminetea-amarae Braun-Blanquet et Tüxen, 1943, Class [16, 25, 26]. Following Bardat and Hauguel [26] and depending on the local situations the bryophytes communities can then be linked to theCaricion remotae Kästner, 1941, Alliance (intraforest communities of oligo-mesotrophic soils), to thePellion endiviifoliae Bardat, 1998, prov.nov. Alliance (neutro-alkaline small streams), or to theRiccardio-Eucladion verticillati Bardat, 1998, prov.nov. Alliance (Ca-rich and thermophilous soils). Following Zechmeister and Mucina [16, 25], Belgian communities can be linked to theCratoneurion commutati Koch, 1928, Alliance (sunny springs with highly oxygen-saturated waters), to theAdiantion Br.-Bl. ex Horvatic, 1934, em. Hoc. Loco Alliance (waters with high Ca contents and high summer temperatures), or to theCaricion remotae Kästner, 1941, Alliance (intraforest communities of oligotrophic waters).The bryological aspects of travertines and their phytosociological composition are well documented in Belgium [1, 4, 16, 21, 22, 27, 28], the Netherlands [18, 27], Italy [29], France [2, 26, 30], and in a wider European context [25]. However, relatively few studies have been undertaken on datasets that consider the driving environmental variables that could influence the distribution of particular species and the species richness of these habitats [11, 31–35].The aims of this research were (1) to learn more about the variables, both physical and chemical, that can affect bryophyte diversity in the surveyed sites; (2) to evidence the most significant variables that can affect the presence or absence of the more characteristic species; (3) to give practical indications on the best ways to maintain or to enhance the quality of this habitat both in Wallonia and the Brussels-Capital Region.


## 2. Materials and Methods

### 2.1. Data Collection

Most of the surveyed sites lie in Wallonia, south Belgium (Figure1), where 62 sites were identified, representing the widest variety of conditions within the 7220 habitat. Wallonia is not a homogeneous region as it consists of 5 subregions characterized by geomorphological specificities (from north to south: sandy Loess Plateau, psammitic Condroz, calcareous Famenne-Calestienne, acidic siliceous Ardennes, and calcareous Lorraine). Petrifying sources are mainly small habitats; however, they are relatively widespread in all these subregions except the Ardennes where it is virtually absent due to the acidic siliceous substrate and in the sandy Loess Plateau where there were also fewer sites. To increase the coverage, we added 5 sites within the Loess region by including some of the Brussels-Capital Region localities.Figure 1
Location of most of the 67 surveyed petrifying sources (some locations are close to each other and cannot be represented here). Belgium (BE) is displayed in grey and the lines inside delimit the 3 administrative regions (Flemish at the north, Brussels-Capital Region just above the Flemish-Walloon border, and Walloon beneath).On each Walloon site a comprehensive sampling regime (random selection of all known locations) was undertaken during autumn, 2012; the bryophytes were identified in the laboratory (A. Sotiaux). Sampling of the 5 locations in the Brussels-Capital Region was undertaken during autumn, 2014, with bryophytes identified in the laboratory (J.-M. Couvreur). We collected bryophytes that are directly associated with areas of travertine and to avoid “noise” in the final dataset we also dropped before analysis species that were not directly related to the 7220 habitats. Moreover, as it is sometimes difficult to assess the rate of abundance or cover by these bryophytes especially in this kind of rather heterogeneous habitat, we decided to only mention the presence/absence of these species.Distance from the spring was not measured but the physical and physicochemical variables, including slope, orientation, canopy cover, and water chemistry, were measured at the location of each bryophyte sample. The physical variables were measured first, including slope, orientation, and canopy cover. Slope was estimated visually and classified into three groups: low (0.5–7.5%), moderate (7.5–20%), and steep (>20%). The orientation was measured using a compass (the four cardinal points plus the four intermediate ones). The percentage of canopy cover (%) was measured visually. Water chemistry, the second group of variables, was measured at each site on the same day that the bryophytes were collected. The water sample was obtained by collecting one litre of water flushing through the site in a glass bottle. The glass bottles were kept cool by storing them in cool boxes with freeze packs, until they were transferred to a laboratory fridge. The pH was measured in the lab using a WTW197i multimeter; other physicochemical variables were measured using a DR3900 Hach Lange photometer: conductivity (μS/cm), nitrates (N-NO3 mg·L−1), ammonium (N-NH4 mg·L−1), soluble orthophosphates (P-PO4 mg·L−1), nitrites (N-NO2 mg·L−1), water hardness (dH degrees), calcium (Ca mg·L−1), and magnesium (Mg mg·L−1). Information including locality, date, location (X and Y Lambert 1972 coordinates), and biogeographical region (Atlantic or Continental mentioned in the variable “Zone”) was also recorded at each site.


### 2.2. Analysis

All of the analyses were performed with R 3.2.1 [36]. We removed one dataset from the analysis (Leffe6) for which NO2 and NO3 values were completely unrealistic, even in highly eutrophicated waters. Four missing values in three different explanatory variables (two for PO4, one for Ca, and one for Mg) were modelled because the AIC approaches we intended to use are incompatible with missing values. We built three Gaussian linear models, one for each variable with missing values, with the other environmental variables as predictors; however, the species data were not used. The missing data were replaced by the predicted value from these models.The relationship between bryophyte community composition (presence/absence matrix) and the environmental factors was studied with a Hellinger transformation based Redundancy Analysis (tb-RDA [37]) performed with vegan 2.3-1 package [38]. We checked that the environmental variables were able to explain at least a part of the variance of the species matrix with a global permutation test (ANOVA function in vegan, 999 permutations). We then applied a simple forward selection procedure based on sequential permutation tests (ordistep function from vegan) to identify the environmental variables that are most related to species composition.The relationship between species richness (total number of species) and the environmental predictors was analysed with a Poisson Generalized Linear Model (GLM). Binomial GLMs were used to characterize the presence/absence of a few individual species relative to the environmental predictors. These binomial GLMs were built only for species that were present on 20 of the 67 sites (Palustriella commutata,Eucladium verticillatum,Cratoneuron filicinum,Pellia endiviifolia, andBrachythecium rivulare).For all GLMs, model conditions (linearity, distribution, and outliers) were checked with residuals plots and overdispersion was checked for the Poisson GLM. Multicollinearity between the explanatory variables was checked graphically and by computing Variance Inflation Factors (VIFs). If necessary, some of the explanatory variables were dropped in order to keep the VIFs < 5. We centred the pH explanatory variable on its mean value to avoid predictions at the intercept for pH = 0, that is, completely outside the range of observed values. The absence of spatial correlation of the models residuals was checked with splines correlograms.In order to determine the most important explanatory variables for these GLMs, we applied an AICc based model selection procedure as described by Burnham and Anderson [39] and shortly summarized hereafter. GLMs with all possible combinations of explanatory variables were computed. Their AICc and AICc model weights were calculated. The AICc model weight quantify the model selection uncertainty and can be interpreted as the probability for a given model to be selected as the best model (the one with lowest AICc) in a set of models if we could resample the data. Then for each explanatory variable we computed a variable weight as the sum of the AICc weights of the models in which this explanatory variable is present. This variable weight is a measure of the relative importance of the explanatory variables. We interpreted only the explanatory variables with an AICc weight > 0.6. The shrinkage model averaged coefficients were then computed along with their unconditional standard errors. These model averaged coefficients are the mean of the coefficient of all models weighted by the model quality (AICc model weights).For the biological interpretation of the GLMs outputs we plotted graphs, based on model averaged coefficients, for the most important explanatory variables in the range of their observed values. The explanatory variables that are not drawn on the graphs are fixed to their mean value.


## 2.1. Data Collection

Most of the surveyed sites lie in Wallonia, south Belgium (Figure1), where 62 sites were identified, representing the widest variety of conditions within the 7220 habitat. Wallonia is not a homogeneous region as it consists of 5 subregions characterized by geomorphological specificities (from north to south: sandy Loess Plateau, psammitic Condroz, calcareous Famenne-Calestienne, acidic siliceous Ardennes, and calcareous Lorraine). Petrifying sources are mainly small habitats; however, they are relatively widespread in all these subregions except the Ardennes where it is virtually absent due to the acidic siliceous substrate and in the sandy Loess Plateau where there were also fewer sites. To increase the coverage, we added 5 sites within the Loess region by including some of the Brussels-Capital Region localities.Figure 1
Location of most of the 67 surveyed petrifying sources (some locations are close to each other and cannot be represented here). Belgium (BE) is displayed in grey and the lines inside delimit the 3 administrative regions (Flemish at the north, Brussels-Capital Region just above the Flemish-Walloon border, and Walloon beneath).On each Walloon site a comprehensive sampling regime (random selection of all known locations) was undertaken during autumn, 2012; the bryophytes were identified in the laboratory (A. Sotiaux). Sampling of the 5 locations in the Brussels-Capital Region was undertaken during autumn, 2014, with bryophytes identified in the laboratory (J.-M. Couvreur). We collected bryophytes that are directly associated with areas of travertine and to avoid “noise” in the final dataset we also dropped before analysis species that were not directly related to the 7220 habitats. Moreover, as it is sometimes difficult to assess the rate of abundance or cover by these bryophytes especially in this kind of rather heterogeneous habitat, we decided to only mention the presence/absence of these species.Distance from the spring was not measured but the physical and physicochemical variables, including slope, orientation, canopy cover, and water chemistry, were measured at the location of each bryophyte sample. The physical variables were measured first, including slope, orientation, and canopy cover. Slope was estimated visually and classified into three groups: low (0.5–7.5%), moderate (7.5–20%), and steep (>20%). The orientation was measured using a compass (the four cardinal points plus the four intermediate ones). The percentage of canopy cover (%) was measured visually. Water chemistry, the second group of variables, was measured at each site on the same day that the bryophytes were collected. The water sample was obtained by collecting one litre of water flushing through the site in a glass bottle. The glass bottles were kept cool by storing them in cool boxes with freeze packs, until they were transferred to a laboratory fridge. The pH was measured in the lab using a WTW197i multimeter; other physicochemical variables were measured using a DR3900 Hach Lange photometer: conductivity (μS/cm), nitrates (N-NO3 mg·L−1), ammonium (N-NH4 mg·L−1), soluble orthophosphates (P-PO4 mg·L−1), nitrites (N-NO2 mg·L−1), water hardness (dH degrees), calcium (Ca mg·L−1), and magnesium (Mg mg·L−1). Information including locality, date, location (X and Y Lambert 1972 coordinates), and biogeographical region (Atlantic or Continental mentioned in the variable “Zone”) was also recorded at each site.


## 2.2. Analysis

All of the analyses were performed with R 3.2.1 [36]. We removed one dataset from the analysis (Leffe6) for which NO2 and NO3 values were completely unrealistic, even in highly eutrophicated waters. Four missing values in three different explanatory variables (two for PO4, one for Ca, and one for Mg) were modelled because the AIC approaches we intended to use are incompatible with missing values. We built three Gaussian linear models, one for each variable with missing values, with the other environmental variables as predictors; however, the species data were not used. The missing data were replaced by the predicted value from these models.The relationship between bryophyte community composition (presence/absence matrix) and the environmental factors was studied with a Hellinger transformation based Redundancy Analysis (tb-RDA [37]) performed with vegan 2.3-1 package [38]. We checked that the environmental variables were able to explain at least a part of the variance of the species matrix with a global permutation test (ANOVA function in vegan, 999 permutations). We then applied a simple forward selection procedure based on sequential permutation tests (ordistep function from vegan) to identify the environmental variables that are most related to species composition.The relationship between species richness (total number of species) and the environmental predictors was analysed with a Poisson Generalized Linear Model (GLM). Binomial GLMs were used to characterize the presence/absence of a few individual species relative to the environmental predictors. These binomial GLMs were built only for species that were present on 20 of the 67 sites (Palustriella commutata,Eucladium verticillatum,Cratoneuron filicinum,Pellia endiviifolia, andBrachythecium rivulare).For all GLMs, model conditions (linearity, distribution, and outliers) were checked with residuals plots and overdispersion was checked for the Poisson GLM. Multicollinearity between the explanatory variables was checked graphically and by computing Variance Inflation Factors (VIFs). If necessary, some of the explanatory variables were dropped in order to keep the VIFs < 5. We centred the pH explanatory variable on its mean value to avoid predictions at the intercept for pH = 0, that is, completely outside the range of observed values. The absence of spatial correlation of the models residuals was checked with splines correlograms.In order to determine the most important explanatory variables for these GLMs, we applied an AICc based model selection procedure as described by Burnham and Anderson [39] and shortly summarized hereafter. GLMs with all possible combinations of explanatory variables were computed. Their AICc and AICc model weights were calculated. The AICc model weight quantify the model selection uncertainty and can be interpreted as the probability for a given model to be selected as the best model (the one with lowest AICc) in a set of models if we could resample the data. Then for each explanatory variable we computed a variable weight as the sum of the AICc weights of the models in which this explanatory variable is present. This variable weight is a measure of the relative importance of the explanatory variables. We interpreted only the explanatory variables with an AICc weight > 0.6. The shrinkage model averaged coefficients were then computed along with their unconditional standard errors. These model averaged coefficients are the mean of the coefficient of all models weighted by the model quality (AICc model weights).For the biological interpretation of the GLMs outputs we plotted graphs, based on model averaged coefficients, for the most important explanatory variables in the range of their observed values. The explanatory variables that are not drawn on the graphs are fixed to their mean value.


## 3. Results

### 3.1. Dataset

The results of the field campaign are presented in Table1(a) (physicochemical variables) and Table 1(b) (species) as two matrices with the 67 sites as lines and the species or environmental factors as columns. The “Leffe6” site is mentioned; however, it was removed due to “out of range” values of NO2 and NO3. The 4 missing values were replaced by their predicted ones in the final matrix by using an explanatory GLM model based on the other variables. These values are, respectively, 0.055 and 0.058 mg·L−1 PO4 for sites MRT1 and MRT2, respectively, 51.06 mg·L−1 Ca for site GVR2 and 25.52 mg·L−1 Mg for site MRT4.Table 1
(a) Dataset of 67 surveyed locations with their measured physicochemical variables. Conductivity is inµS; hardness is in dH degrees; slope and canopy cover are in percentages; NO3, NH4, PO4, NO2, Ca, and Mg are in mg⋅L−1. (b) Dataset of 67 surveyed locations with the presence/absence data of the bryophytes species (characteristic and companion species). Species are: Aneura pinguis, Brachythecium rivulare, Bryum pseudotriquetrum, Conocephalum conicum, Cratoneuron filicinum, Ctenidium molluscum, Dichodontium pellucidum, Didymodon tophaceus, Eucladium verticillatum, Fissidens adianthoides, Fissidens crassipes, Fissidens taxifolius, Leiocolea badensis, Gymnostomum calcareum, Palustriella commutata, Pellia endiviifolia, Philonotis calcarea, Plagiomnium ellipticum, Plagiomnium rostratum, Plagiomnium undulatum, Platyhypnidium riparioides, Preissia quadrata, and Oxyrrhynchium hians.
(a) Site Nb Zone Location pH Conductivity NO3 NH4 PO4 NO2 Hardness Ca Mg Slope Orientation Canopy cover 1 atl Hautmont1 7.61 778 7.97 0 0.053 0.011 25.4 157 14.8 Moderate NW 80 2 atl Hautmont2 7.88 749 0.704 0.028 0.089 0.011 23.2 145 12.7 Low NE 90 3 atl Hautmont3 8.04 731 7.46 0.033 0.165 0.045 23.6 138 18.5 Low NE 80 4 atl Hautmont4 8.08 665 2.5 0.053 0.078 0.028 21.1 131 11.7 Low NW 70 5 atl Hautmont5 7.87 530 2.31 0.194 0.461 0.264 16.1 92.6 13.3 Low N 60 6 atl Ophain1 7.07 756 7.55 0.015 0.04 0.011 25.1 157 13.3 Low N 90 7 atl Ophain2 7.92 790 5.52 0.026 0.042 0.013 21 132 10.7 Low W 90 8 cont Hastière1 7.52 799 9 0.021 0 0.007 26.7 183 4,35 Steep NW 0 9 cont Hastière2 8.25 594 8.86 0.016 0.055 0.007 19.7 140 0 Moderate NW 0 10 cont Hastière3 8.16 505 12.3 0.016 0 0.006 15.4 93.5 9.72 Steep W 50 11 cont Hastière4 8.21 555 10.5 0.017 0.014 0.009 17.5 98.4 15.9 Moderate SW 100 13 cont Leffe1 8.27 592 4.34 0.026 0.076 4.35 19 114 13 Low W 50 14 cont Leffe3 8.13 595 0.074 0.022 0.0262 0.076 18.7 109 14.8 Low W 100 15 cont Leffe5 7.51 636 0.03 0.073 0.161 0.022 21.6 124 18 Low E 0 16 cont Leffe6 8.18 560 19 0.018 0.005 19.1 17.5 93.3 19.3 Steep S 50 17 cont Leffe7 8.22 599 4.95 0.032 0.057 5 18.6 113 11.5 Low W 0 18 cont Vodelée1 7.13 720 6.19 0.017 0.064 6.22 24.4 148 15.8 Steep NW 30 19 cont Denée1 8.2 634 4.83 0.02 0.06 4.86 20.1 117 16.2 Moderate SE 100 20 cont Denée2 8.07 700 5.16 0.027 0.115 5.18 26.1 151 21.5 Moderate S 100 21 atl Mariemont: MRT1 7.76 907 4.98 0.03 NA 0.013 30.6 188 18.2 Low S 100 22 atl Mariemont: MRT2 8.12 879 2.16 0.035 NA 0.015 27.8 173 15.1 Moderate SE 100 23 atl Mariemont: MRT3 8.19 672 1.7 0.021 0.092 0.014 22.2 143 9.36 Low S 100 24 atl Mariemont: MRT4 8.09 659 0.779 0.033 0.029 0.02 19.1 136 NA Low SE 100 25 atl Givry: GVR1 8.01 658 6.69 0.042 0.042 0.015 20.9 119 18.2 Low W 100 26 atl Givry: GVR2 8.06 618 2.84 0.446 0.061 0.009 4.09 NA 17.7 Low W 100 27 cont Han-sur-Lesse: ST-MART 7.23 673 2.15 0.019 0.118 0.009 23.9 134 22.6 Low SW 100 28 cont Tellin: CHNT 7.78 773 7.82 0.021 0.014 0.009 26.7 150 24.3 Moderate S 100 29 cont Neuville: NVL 7.22 687 5.94 0.019 0.009 0.014 25.1 150 17.8 Steep E 100 30 cont Fairon1 8.08 466 6.84 0.016 0 0.011 16.4 83.4 20.3 Steep SE 100 31 cont Fairon2 7.2 497 7.43 0.004 0.003 0.012 15.6 87.7 14.2 Steep SE 100 32 cont Fairon3 6.22 463 6.27 0.012 0 0.012 15.3 84.4 15.2 Steep SE 100 33 cont Pont1 8.09 733 9.33 0.01 0.002 0.006 25.1 136 26.1 Steep NE 50 34 cont Pont2 8.32 578 11.1 0.013 0 0.014 18.1 86.6 25.7 Steep NE 50 35 cont Pont3 8 630 12.1 0.007 0.004 0 20.4 89.4 34 Steep SE 50 36 cont Tour1 7.42 728 4.57 0.009 0 0.013 24.3 123 30.3 Low NE 100 37 cont Oneux1 8.1 562 5.11 0.065 0.083 0.069 16.9 87.3 22.5 Moderate NE 10 38 cont Oneux2 7.42 607 5 0.007 0.011 0.009 19.5 95.9 26.1 Moderate SE 80 39 cont Oneux3 7.57 684 5.96 0.009 0 0 22.7 108 32.6 Steep S 100 40 cont Barvaux: NUT1 7.88 670 0.831 0.018 0.015 0.017 22.9 131 19.3 Low NW 80 41 cont Barvaux: NUT2 7.63 585 0.67 0.004 0.007 0.018 19.2 109 17.3 Moderate NW 80 42 cont Barvaux: NUT3 7.66 675 0.385 0.018 0.01 0.017 22.2 122 22.1 Low NW 100 43 cont Barvaux: NUT4 7.91 621 1.33 0.013 0.005 0.015 20.4 121 14.5 Moderate NW 50 44 cont Barvaux: NUT5 8.02 582 0.529 0.019 0.037 0.016 19.1 108 17 Low W 20 45 cont Barvaux: NUT6 8.03 653 0.5 0.025 0.021 0.02 7.55 27.8 15.8 Low W 100 46 cont Montauban1 7.78 416 1.1 0 0.004 0.013 15.6 93.7 10.8 Steep SW 0 47 cont Mon2 7.81 418 1.26 0 0.003 0.012 14.6 89.6 8.77 Steep S 0 48 cont Rougeeau1 7.93 437 0.936 0 0.003 0.013 15.3 99 6.25 Steep SE 0 49 cont Rougeeau2 7.93 426 0.908 0 0.009 0.012 15.9 96 10.7 Steep SE 0 50 cont Huombois1 7.95 450 4.15 0 0.003 0.012 15.2 91.1 10.4 Steep W 10 51 cont Bel1 7.82 418 0.364 0.012 0 0.01 14.6 88.1 9.71 Steep SE 0 52 cont Bel2 7.99 499 3.9 0.015 0.005 0.011 17 106 9.36 Steep SE 0 53 cont Bel3 7.41 593 5.26 0.015 0.003 0.012 21.6 136 10.7 Steep S 0 54 cont Bel4 7.54 452 2.63 0.009 0.004 0.01 16.2 101 8.57 Steep SE 80 55 cont Bel5 7.73 460 2.91 0.009 0.015 0.013 16.6 103 9.43 Steep N 0 56 cont Saint-Mard: StMA1 8.11 391 1.15 0.026 0.059 0.021 13.1 83.8 5.75 Low W 50 57 cont Saint-Mard: StMA2 8.06 365 0.986 0.012 0.027 0.017 12.3 76.5 6.68 Low NW 90 58 cont Saint-Mard: StMA3 7.86 397 1.06 0.014 0.019 0.016 13.6 83.6 7.96 Moderate NW 100 59 cont Saint-Mard: PtCAM1 7.23 503 0.242 0.003 0.132 0.005 17.7 112 8.54 Low NW 70 60 cont Saint-Mard: PtCAM2 7.83 494 0.307 0.031 0.072 0.018 18.3 107 14.3 Low SW 80 61 cont Saint-Mard: HARN 8.09 377 0.224 0.006 0.027 0.019 13.9 81.9 10.5 Steep N 80 62 atl Poelbos (RBC) 8.2 908 0.236 0.031 0.042 0.013 30.1 163 31.5 Moderate S 80 63 atl Laerbeek (cascade) 7.94 892 0.488 0.034 0.021 0.004 28.7 158 28.4 Low S 80 64 atl Laerbeek (Kwel) 7.89 1085 0.598 0.047 0.022 0.011 32.3 172 65.5 Low S 70 65 atl Bois de Dieleghem (RBC) 8.07 1007 1.36 0.041 0.043 0.019 46.1 251 47.1 Low S 80 66 atl Jardin Massart (RBC) 7.8 1058 1.69 0.046 0.053 0.005 30.9 170 30.6 Moderate NW 70 67 cont Hoyoux (Galoux) 8.26 557 5.083 0.02 0.031 0.007 32.6 97.4 20.1 Low NW 60 68 cont Triffoy (Galoux) 7.83 608 5.144 0.026 0.016 0.009 34.7 96.3 25.9 Moderate NE 60  (b) Site Nb Zone Location Aneu_pin Brach_riv Bryu_pse Cono_con Crat_fil Cten_mol Dich_pel Did_toph Eucl_vert Fiss_adi Fiss_cra Fiss_tax Leio_bad Gymn_cal Pal_com Pell_end phi_calc Pla_ell Plag_ros Plag_und Plat_rip Pre_qua Oxy_hians 1 atl Hautmont1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 2 atl Hautmont2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 atl Hautmont3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 atl Hautmont4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 atl Hautmont5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 atl Ophain1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 7 atl Ophain2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 8 cont Hastière1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 9 cont Hastière2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 cont Hastière3 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 11 cont Hastière4 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 13 cont Leffe1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 14 cont Leffe3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 cont Leffe5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 cont Leffe6 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 17 cont Leffe7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 cont Vodelée1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 19 cont Denée1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 20 cont Denée2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 atl Mariemont: MRT1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 22 atl Mariemont: MRT2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 atl Mariemont: MRT3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 atl Mariemont: MRT4 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 25 atl Givry: GVR1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 26 atl Givry: GVR2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 cont Han-sur-Lesse: ST-MART 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 28 cont Tellin: CHNT 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 29 cont Neuville: NVL 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 30 cont Fairon1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 31 cont Fairon2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 cont Fairon3 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 33 cont Pont1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 34 cont Pont2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 35 cont Pont3 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 36 cont Tour1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 37 cont Oneux1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 38 cont Oneux2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 39 cont Oneux3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 40 cont Barvaux: NUT1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41 cont Barvaux: NUT2 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 42 cont Barvaux: NUT3 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 43 cont Barvaux: NUT4 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 44 cont Barvaux: NUT5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 45 cont Barvaux: NUT6 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 46 cont Montauban1 1 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 47 cont Mon2 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 48 cont Rougeeau1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 49 cont Rougeeau2 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 50 cont Huombois1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 51 cont Bel1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 52 cont Bel2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 53 cont Bel3 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 54 cont Bel4 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 0 55 cont Bel5 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 56 cont Saint-Mard: StMA1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 57 cont Saint-Mard: StMA2 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 58 cont Saint-Mard: StMA3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 59 cont Saint-Mard: PtCAM1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 60 cont Saint-Mard: PtCAM2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 61 cont Saint-Mard: HARN 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 62 atl Poelbos (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 63 atl Laerbeek (cascade) 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 64 atl Laerbeek (Kwel) 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 65 atl Bois de Dieleghem (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 66 atl Jardin Massart (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 67 cont Hoyoux (Galoux) 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 68 cont Triffoy (Galoux) 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0Three pairs of explanatory variables were highly correlated: conductivity-Ca (0.8), conductivity-water hardness (0.77), and water hardness-Ca (0.85). We decided to drop the variables conductivity and water hardness which were highly correlated to the Ca concentration and to keep the last one for the next step analysis.


### 3.2. Key Variables of Species and Sites Assemblages

The tb-RDA analysis (Table2) using the list of previously retained quantitative and qualitative variables (pH, NO3, NH4, PO4, NO2, Ca, Mg, slope converted into numerical values from 1 to 3, canopy cover and orientation). The first unconstrained axis (PCA1) explains a rather high amount of variation, almost comparable to the first constrained axis,RDA1. This suggests that there is another environmental variable, not measured during this study, which could influence the species composition of the communities (see Section 4). Nevertheless the variables explain 36.5% of the variation and a Monte Carlo permutation test executed on this RDA delivers a highly significant value for the model (P<0.001) that confirms the global model is relevant.Table 2
Results of the tb-RDA analysis.
Dataset Percentage of variation explained by the variables (constrained variation) Percentage explained by first axis inside constrained variation Percentage explained by second axis inside constrained variation With outliers and orientation variable 36.5 44.9 18.1 Without outliers and with orientation variable 38.3 42.8 15.4 With outliers and without orientation variable 28.3 55.9 17.7Inside the constrained variance the first axis explained 44.9% (= eigenvalue RDA1 = 0.4282/0.9524) of this constrained variance and the second axis 18.1% (= eigenvalue RDA2 = 0.1721/0.9524). The use of the same dataset without the two outliers yields approximately the same results (38.3% of constrained variation, 42.7% for the first axis, and 15.4% for the second axis). As the parameter orientation is not retained by the forward selection model and because it produces a poorly readable figure, we also tested the same model without the orientation variable (Figure2). The results are the following: 28.3% of constrained variation, 55.9% for the first axis, and 17.7% for the second axis.Figure 2
Plot of the tb-RDA analysis with (a) and without (b) orientation variable.
(a) (b)The RDA analysis is potted both with and without the orientation variable (Figure2). The scaling 2 option [37] allows the following comments and shows that slope, NO3, NH4, PO4, and canopy cover play an important role in the dispersion of the sites along the first axis.The two variables NH4 and PO4 are very closely correlated, suggesting that they can act in a similar way on the species assemblages and between factors themselves; however, NO3 is negatively correlated with both NH4 and PO4. The two characteristic speciesEucladium verticillatum andPalustriella commutata are close together suggesting they appear both on sites with higher slope and lower cover of canopy and lower NH4 and PO4 concentrations.Pellia endiviifolia shows a positive relationship with NO3 whereasBrachythecium rivulare andOxyrrhynchium hians (Hedw.) Loeske are weakly correlated with PO4, NH4, and high pH values.Bryum pseudotriquetrum is negatively correlated with canopy cover. Most other species are clustered together away from these extremes. They show mostly shorter projections, indicating that they are either present in most of the sites or related to intermediate ecological conditions; however, they could also be related to some variables on third or higher axis which is not displayed.The result of the forward model selection of the RDA retained a model with (sorted by decreasing importance) slope, Mg, Ca, and canopy cover (spe ~ slope + Mg + Ca + cover). The same analysis without the outliers (“Haumont5” and “Givry2”) generates a slightly different model with slope, canopy cover, Mg, and Ca as the most significant factors (spe ~ slope + cover + Mg + Ca).


### 3.3. Species Richness

The results show (Table3) that only two explanatory variables (canopy cover and PO4) are strongly supported by the data (variable weight = 0.992 and 0.960, resp.) while two other ones are moderately supported by the data (NH4 and Mg) with a weight of, respectively, 0.709 and 0.650. All but the Mg variables are negatively correlated with species richness.Table 3
Results of the AIC stepwise model selection for species richness showing the best significant variables selected based on their weight using the complete dataset (columns on the left) or the data without 2 possible outliers (columns on the right).
Variable Weight of the variable Averaged coefficient Unconditional standard error Full model Model without possible outliers Full model Model without possible outliers Full model Model without possible outliers Cover 0.992 0.992 −0.007 −0.006 0.002 0.002 PO4 0.960 0.983 −7.510 −8.361 2.572 2.479 NH4 0.709 0.300 −5.615 −1.689 4.140 2.414 Mg 0.650 0.548 0.009 0.007 0.006 0.005 pH_mean 0.587 0.616 −0.186 −0.200 0.128 0.131 Zonecont 0.309 0.300 0.065 0.058 0.086 0.080 Ca 0.300 0.269 0.001 0.000 0.001 0.001 NO3 0.274 0.276 0.004 0.004 0.006 0.006 NO2 0.230 0.229 −0.002 −0.001 0.014 0.013 Slope moderate 0.087 0.090 0.007 0.008 0.018 0.019 Slope steep 0.087 0.09 0.005 0.008 0.021 0.023To check the potential influence of outliers we performed the same analysis after excluding Haumont5 and Givry2 sites which have very high PO4 and NH4 values. The model selection results are comparable to the first analysis including outliers; AIC retains only canopy cover and PO4 as the main explanatory variables (Table 3) (columns on the right for each parameter).Figures3 and 4 show the results of two predicted scenarios based on the complete model (2 outliers included) with the mean values of other variables. Figure 3 represents the relation between the PO4 concentration and the species richness and Figure 4 represents the relation between the canopy cover and the species richness. It can be seen that for the canopy cover the expected number of species rises by 2 or 3 species (going approximately from 2 to 4 or even 5) when the cover falls from 100 to 0%. In the case of the PO4 concentration the expected number of species falls to zero when the PO4 concentration rises from 0 to 0.5 mg·L−1 or more.Figure 3
Prediction of species richness in relation to PO4 concentration based on the coefficients of the best models selected by AIC stepwise selection.Figure 4
Prediction of species richness in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection.


### 3.4. Presence/Absence of Characteristic and Companion Species

For each species we began by excluding the variables that were highly correlated using the VIF (Variance Inflation Factor) values. The only variable to exclude from the five species was orientation and thus the binomial GLM analysis included the following explanatory variables: zone, NO2, Mg, pH_mean, Ca, NH4, NO3, canopy cover, slope, and PO4.


#### 3.4.1.Palustriella commutata

The most important variables delivered by the AIC procedure are “steep slope” and “moderate slope” and to a lesser extend “canopy cover” (Table4). The frequency ofPalustriella commutata tends to increase when the slope is steeper and decreases when canopy cover is higher (Figure 5).Table 4
Results of the AIC stepwise model selection for probability of presence ofPalustriella commutata showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Moderate slope 0.902 1.116 1.002 Steep slope 0.902 3.152 1.010 Cover 0.797 −0.022 0.011 pH_mean 0.592 −1.293 0.947 NH4 0.387 −13.210 14.475 NO3 0.379 0.063 0.066 Ca 0.336 −0.006 0.007 PO4 0.283 1.874 3.610 NO2 0.254 −0.048 0.091 Mg 0.248 −0.004 0.014 Zonecont 0.243 0.0097 0.357Figure 5
Prediction of presence ofPalustriella commutata in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different slopes: low slope (solid line), moderate slope (dashed line), and steep slope (dotted line).


#### 3.4.2.Eucladium verticillatum

The most important variables delivered by the AIC procedure are canopy cover and NH4, both are negatively correlated with the probability of the presence of the species (Table 5). The frequency ofEucladium verticillatum tends to decrease when the canopy cover increases and for higher concentrations of NH4, however the size of the effect is relatively limited as shown on Figure 6.Table 5
Results of the AIC stepwise model selection for probability of presence ofEucladium verticillatum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Canopy cover 0.931 −0.031 0.013 NH4 0.714 −80.324 52.086 PO4 0.523 −17.784 15.728 NO2 0.516 0.311 0.259 pH_mean 0.453 −0.684 0.625 Slope moderate 0.445 −0.256 0.675 Slope steep 0.445 0.862 0.772 Zonecont 0.383 6.353 878.235 NO3 0.277 −0.023 0.041 Mg 0.250 0.003 0.015 Ca 0.241 0.000 0.005Figure 6
Prediction of presence ofEucladium verticillatum in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different levels of NH4: maximum concentration in the dataset (dotted line), mean concentration in the dataset (dashed line), and minimum concentration in the dataset (solid line).


#### 3.4.3.Cratoneuron filicinum

The only important variable delivered by the AIC procedure is the Mg concentration (positive relationship) and to a lesser degree NH4 (negative relationship) (Table 6, Figure 7). The results remain similar after excluding the “Laarbeek (Kwel)” site whose outlying value (65.5 mg·L−1) could have unduly influenced the correlation.Table 6
Results of the AIC stepwise model selection for probability of presence ofCratoneuron filicinum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Mg 0.986 0.127 0.049 NH4 0.734 −21.408 17.105 PO4 0.339 −2.269 3.217 Ca 0.324 −0.003 0.004 Cover 0.322 −0.002 0.003 pH_mean 0.261 −0.110 0.227 Zonecont 0.257 −0.019 0.231 NO2 0.239 0.012 0.049 NO3 0.238 0.005 0.022 Slope moderate 0.135 0.09 0.133 Slope steep 0.135 −0.018 0.121Figure 7
Prediction of presence ofCratoneuron filicinum in relation to Mg concentration based on the coefficients of the best models selected by AIC stepwise selection.


#### 3.4.4.Pellia endiviifolia

The most important variables delivered by the AIC procedure are PO4 (negative relationship) and NO3 (positive relationship) (Table 7; Figure 8).Table 7
Results of the AIC stepwise model selection for probability of presence ofPellia endiviifolia showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error PO4 0.922 −28.250 13.310 NO3 0.885 0.251 0.113 Ca 0.650 −0.015 0.010 NO2 0.448 0.168 0.157 NH4 0.422 −4.857 5.898 Zonecont 0.419 −0.660 0.677 Cover 0.339 −0.003 0.004 Mg 0.257 0.004 0.010 pH_mean 0.240 −0.095 0.273 Slope moderate 0.233 0.252 0.269 Slope steep 0.233 0.296 0.319Figure 8
Prediction of presence ofPellia endiviifolia in relation to PO4 concentration based on the coefficients of the best models selected by AIC stepwise selection with the full dataset.


#### 3.4.5.Brachythecium rivulare

The only important variable delivered by the AIC procedure is the Ca concentration (positive relationship) (Table8). However Figure 9 shows that this correlation is weak and is based almost entirely on the outlying value (251 mg·L−1) of the “Bois de Dieleghem” site. It would thus be sensible to not pay too much attention to this correlation.Table 8
Results of the AIC stepwise model selection for probability of presence ofBrachythecium rivulare showing the best significant variables selected based on their weight.
Variable Weight of the variable Averaged coefficient Unconditional standard error Ca 0.894 0.023 0.010 PO4 0.579 −9.225 7.229 Slope moderate 0.468 −0.193 0.369 Slope steep 0.468 −0.897 0.650 pH_mean 0.354 0.354 0.414 NH4 0.351 −7.123 10.000 Cover 0.292 0.002 0.003 Zonecont 0.285 −0.149 0.293 Mg 0.262 0.004 0.010 NO3 0.251 0.009 0.025 NO2 0.240 0.007 0.054Figure 9
Prediction of presence ofBrachythecium rivulare in relation to Ca concentration based on the coefficients of the best models selected by AIC stepwise selection.


## 3.1. Dataset

The results of the field campaign are presented in Table1(a) (physicochemical variables) and Table 1(b) (species) as two matrices with the 67 sites as lines and the species or environmental factors as columns. The “Leffe6” site is mentioned; however, it was removed due to “out of range” values of NO2 and NO3. The 4 missing values were replaced by their predicted ones in the final matrix by using an explanatory GLM model based on the other variables. These values are, respectively, 0.055 and 0.058 mg·L−1 PO4 for sites MRT1 and MRT2, respectively, 51.06 mg·L−1 Ca for site GVR2 and 25.52 mg·L−1 Mg for site MRT4.Table 1
(a) Dataset of 67 surveyed locations with their measured physicochemical variables. Conductivity is inµS; hardness is in dH degrees; slope and canopy cover are in percentages; NO3, NH4, PO4, NO2, Ca, and Mg are in mg⋅L−1. (b) Dataset of 67 surveyed locations with the presence/absence data of the bryophytes species (characteristic and companion species). Species are: Aneura pinguis, Brachythecium rivulare, Bryum pseudotriquetrum, Conocephalum conicum, Cratoneuron filicinum, Ctenidium molluscum, Dichodontium pellucidum, Didymodon tophaceus, Eucladium verticillatum, Fissidens adianthoides, Fissidens crassipes, Fissidens taxifolius, Leiocolea badensis, Gymnostomum calcareum, Palustriella commutata, Pellia endiviifolia, Philonotis calcarea, Plagiomnium ellipticum, Plagiomnium rostratum, Plagiomnium undulatum, Platyhypnidium riparioides, Preissia quadrata, and Oxyrrhynchium hians.
(a) Site Nb Zone Location pH Conductivity NO3 NH4 PO4 NO2 Hardness Ca Mg Slope Orientation Canopy cover 1 atl Hautmont1 7.61 778 7.97 0 0.053 0.011 25.4 157 14.8 Moderate NW 80 2 atl Hautmont2 7.88 749 0.704 0.028 0.089 0.011 23.2 145 12.7 Low NE 90 3 atl Hautmont3 8.04 731 7.46 0.033 0.165 0.045 23.6 138 18.5 Low NE 80 4 atl Hautmont4 8.08 665 2.5 0.053 0.078 0.028 21.1 131 11.7 Low NW 70 5 atl Hautmont5 7.87 530 2.31 0.194 0.461 0.264 16.1 92.6 13.3 Low N 60 6 atl Ophain1 7.07 756 7.55 0.015 0.04 0.011 25.1 157 13.3 Low N 90 7 atl Ophain2 7.92 790 5.52 0.026 0.042 0.013 21 132 10.7 Low W 90 8 cont Hastière1 7.52 799 9 0.021 0 0.007 26.7 183 4,35 Steep NW 0 9 cont Hastière2 8.25 594 8.86 0.016 0.055 0.007 19.7 140 0 Moderate NW 0 10 cont Hastière3 8.16 505 12.3 0.016 0 0.006 15.4 93.5 9.72 Steep W 50 11 cont Hastière4 8.21 555 10.5 0.017 0.014 0.009 17.5 98.4 15.9 Moderate SW 100 13 cont Leffe1 8.27 592 4.34 0.026 0.076 4.35 19 114 13 Low W 50 14 cont Leffe3 8.13 595 0.074 0.022 0.0262 0.076 18.7 109 14.8 Low W 100 15 cont Leffe5 7.51 636 0.03 0.073 0.161 0.022 21.6 124 18 Low E 0 16 cont Leffe6 8.18 560 19 0.018 0.005 19.1 17.5 93.3 19.3 Steep S 50 17 cont Leffe7 8.22 599 4.95 0.032 0.057 5 18.6 113 11.5 Low W 0 18 cont Vodelée1 7.13 720 6.19 0.017 0.064 6.22 24.4 148 15.8 Steep NW 30 19 cont Denée1 8.2 634 4.83 0.02 0.06 4.86 20.1 117 16.2 Moderate SE 100 20 cont Denée2 8.07 700 5.16 0.027 0.115 5.18 26.1 151 21.5 Moderate S 100 21 atl Mariemont: MRT1 7.76 907 4.98 0.03 NA 0.013 30.6 188 18.2 Low S 100 22 atl Mariemont: MRT2 8.12 879 2.16 0.035 NA 0.015 27.8 173 15.1 Moderate SE 100 23 atl Mariemont: MRT3 8.19 672 1.7 0.021 0.092 0.014 22.2 143 9.36 Low S 100 24 atl Mariemont: MRT4 8.09 659 0.779 0.033 0.029 0.02 19.1 136 NA Low SE 100 25 atl Givry: GVR1 8.01 658 6.69 0.042 0.042 0.015 20.9 119 18.2 Low W 100 26 atl Givry: GVR2 8.06 618 2.84 0.446 0.061 0.009 4.09 NA 17.7 Low W 100 27 cont Han-sur-Lesse: ST-MART 7.23 673 2.15 0.019 0.118 0.009 23.9 134 22.6 Low SW 100 28 cont Tellin: CHNT 7.78 773 7.82 0.021 0.014 0.009 26.7 150 24.3 Moderate S 100 29 cont Neuville: NVL 7.22 687 5.94 0.019 0.009 0.014 25.1 150 17.8 Steep E 100 30 cont Fairon1 8.08 466 6.84 0.016 0 0.011 16.4 83.4 20.3 Steep SE 100 31 cont Fairon2 7.2 497 7.43 0.004 0.003 0.012 15.6 87.7 14.2 Steep SE 100 32 cont Fairon3 6.22 463 6.27 0.012 0 0.012 15.3 84.4 15.2 Steep SE 100 33 cont Pont1 8.09 733 9.33 0.01 0.002 0.006 25.1 136 26.1 Steep NE 50 34 cont Pont2 8.32 578 11.1 0.013 0 0.014 18.1 86.6 25.7 Steep NE 50 35 cont Pont3 8 630 12.1 0.007 0.004 0 20.4 89.4 34 Steep SE 50 36 cont Tour1 7.42 728 4.57 0.009 0 0.013 24.3 123 30.3 Low NE 100 37 cont Oneux1 8.1 562 5.11 0.065 0.083 0.069 16.9 87.3 22.5 Moderate NE 10 38 cont Oneux2 7.42 607 5 0.007 0.011 0.009 19.5 95.9 26.1 Moderate SE 80 39 cont Oneux3 7.57 684 5.96 0.009 0 0 22.7 108 32.6 Steep S 100 40 cont Barvaux: NUT1 7.88 670 0.831 0.018 0.015 0.017 22.9 131 19.3 Low NW 80 41 cont Barvaux: NUT2 7.63 585 0.67 0.004 0.007 0.018 19.2 109 17.3 Moderate NW 80 42 cont Barvaux: NUT3 7.66 675 0.385 0.018 0.01 0.017 22.2 122 22.1 Low NW 100 43 cont Barvaux: NUT4 7.91 621 1.33 0.013 0.005 0.015 20.4 121 14.5 Moderate NW 50 44 cont Barvaux: NUT5 8.02 582 0.529 0.019 0.037 0.016 19.1 108 17 Low W 20 45 cont Barvaux: NUT6 8.03 653 0.5 0.025 0.021 0.02 7.55 27.8 15.8 Low W 100 46 cont Montauban1 7.78 416 1.1 0 0.004 0.013 15.6 93.7 10.8 Steep SW 0 47 cont Mon2 7.81 418 1.26 0 0.003 0.012 14.6 89.6 8.77 Steep S 0 48 cont Rougeeau1 7.93 437 0.936 0 0.003 0.013 15.3 99 6.25 Steep SE 0 49 cont Rougeeau2 7.93 426 0.908 0 0.009 0.012 15.9 96 10.7 Steep SE 0 50 cont Huombois1 7.95 450 4.15 0 0.003 0.012 15.2 91.1 10.4 Steep W 10 51 cont Bel1 7.82 418 0.364 0.012 0 0.01 14.6 88.1 9.71 Steep SE 0 52 cont Bel2 7.99 499 3.9 0.015 0.005 0.011 17 106 9.36 Steep SE 0 53 cont Bel3 7.41 593 5.26 0.015 0.003 0.012 21.6 136 10.7 Steep S 0 54 cont Bel4 7.54 452 2.63 0.009 0.004 0.01 16.2 101 8.57 Steep SE 80 55 cont Bel5 7.73 460 2.91 0.009 0.015 0.013 16.6 103 9.43 Steep N 0 56 cont Saint-Mard: StMA1 8.11 391 1.15 0.026 0.059 0.021 13.1 83.8 5.75 Low W 50 57 cont Saint-Mard: StMA2 8.06 365 0.986 0.012 0.027 0.017 12.3 76.5 6.68 Low NW 90 58 cont Saint-Mard: StMA3 7.86 397 1.06 0.014 0.019 0.016 13.6 83.6 7.96 Moderate NW 100 59 cont Saint-Mard: PtCAM1 7.23 503 0.242 0.003 0.132 0.005 17.7 112 8.54 Low NW 70 60 cont Saint-Mard: PtCAM2 7.83 494 0.307 0.031 0.072 0.018 18.3 107 14.3 Low SW 80 61 cont Saint-Mard: HARN 8.09 377 0.224 0.006 0.027 0.019 13.9 81.9 10.5 Steep N 80 62 atl Poelbos (RBC) 8.2 908 0.236 0.031 0.042 0.013 30.1 163 31.5 Moderate S 80 63 atl Laerbeek (cascade) 7.94 892 0.488 0.034 0.021 0.004 28.7 158 28.4 Low S 80 64 atl Laerbeek (Kwel) 7.89 1085 0.598 0.047 0.022 0.011 32.3 172 65.5 Low S 70 65 atl Bois de Dieleghem (RBC) 8.07 1007 1.36 0.041 0.043 0.019 46.1 251 47.1 Low S 80 66 atl Jardin Massart (RBC) 7.8 1058 1.69 0.046 0.053 0.005 30.9 170 30.6 Moderate NW 70 67 cont Hoyoux (Galoux) 8.26 557 5.083 0.02 0.031 0.007 32.6 97.4 20.1 Low NW 60 68 cont Triffoy (Galoux) 7.83 608 5.144 0.026 0.016 0.009 34.7 96.3 25.9 Moderate NE 60  (b) Site Nb Zone Location Aneu_pin Brach_riv Bryu_pse Cono_con Crat_fil Cten_mol Dich_pel Did_toph Eucl_vert Fiss_adi Fiss_cra Fiss_tax Leio_bad Gymn_cal Pal_com Pell_end phi_calc Pla_ell Plag_ros Plag_und Plat_rip Pre_qua Oxy_hians 1 atl Hautmont1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 2 atl Hautmont2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 atl Hautmont3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 atl Hautmont4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 atl Hautmont5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 atl Ophain1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 7 atl Ophain2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 8 cont Hastière1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 9 cont Hastière2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 cont Hastière3 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 11 cont Hastière4 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 13 cont Leffe1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 14 cont Leffe3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 cont Leffe5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 cont Leffe6 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 17 cont Leffe7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 cont Vodelée1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 19 cont Denée1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 20 cont Denée2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 atl Mariemont: MRT1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 22 atl Mariemont: MRT2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 atl Mariemont: MRT3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 atl Mariemont: MRT4 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 25 atl Givry: GVR1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 26 atl Givry: GVR2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 cont Han-sur-Lesse: ST-MART 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 28 cont Tellin: CHNT 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 29 cont Neuville: NVL 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 30 cont Fairon1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 31 cont Fairon2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 cont Fairon3 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 33 cont Pont1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 34 cont Pont2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 35 cont Pont3 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 36 cont Tour1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 37 cont Oneux1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 38 cont Oneux2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 39 cont Oneux3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 40 cont Barvaux: NUT1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41 cont Barvaux: NUT2 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 42 cont Barvaux: NUT3 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 43 cont Barvaux: NUT4 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 44 cont Barvaux: NUT5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 45 cont Barvaux: NUT6 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 46 cont Montauban1 1 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 47 cont Mon2 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 48 cont Rougeeau1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 49 cont Rougeeau2 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 50 cont Huombois1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 51 cont Bel1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 52 cont Bel2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 53 cont Bel3 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 54 cont Bel4 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 0 55 cont Bel5 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 56 cont Saint-Mard: StMA1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 57 cont Saint-Mard: StMA2 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 58 cont Saint-Mard: StMA3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 59 cont Saint-Mard: PtCAM1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 60 cont Saint-Mard: PtCAM2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 61 cont Saint-Mard: HARN 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 62 atl Poelbos (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 63 atl Laerbeek (cascade) 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 64 atl Laerbeek (Kwel) 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 65 atl Bois de Dieleghem (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 66 atl Jardin Massart (RBC) 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 67 cont Hoyoux (Galoux) 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 68 cont Triffoy (Galoux) 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0Three pairs of explanatory variables were highly correlated: conductivity-Ca (0.8), conductivity-water hardness (0.77), and water hardness-Ca (0.85). We decided to drop the variables conductivity and water hardness which were highly correlated to the Ca concentration and to keep the last one for the next step analysis.


## 3.2. Key Variables of Species and Sites Assemblages

The tb-RDA analysis (Table2) using the list of previously retained quantitative and qualitative variables (pH, NO3, NH4, PO4, NO2, Ca, Mg, slope converted into numerical values from 1 to 3, canopy cover and orientation). The first unconstrained axis (PCA1) explains a rather high amount of variation, almost comparable to the first constrained axis,RDA1. This suggests that there is another environmental variable, not measured during this study, which could influence the species composition of the communities (see Section 4). Nevertheless the variables explain 36.5% of the variation and a Monte Carlo permutation test executed on this RDA delivers a highly significant value for the model (P<0.001) that confirms the global model is relevant.Table 2
Results of the tb-RDA analysis.
Dataset Percentage of variation explained by the variables (constrained variation) Percentage explained by first axis inside constrained variation Percentage explained by second axis inside constrained variation With outliers and orientation variable 36.5 44.9 18.1 Without outliers and with orientation variable 38.3 42.8 15.4 With outliers and without orientation variable 28.3 55.9 17.7Inside the constrained variance the first axis explained 44.9% (= eigenvalue RDA1 = 0.4282/0.9524) of this constrained variance and the second axis 18.1% (= eigenvalue RDA2 = 0.1721/0.9524). The use of the same dataset without the two outliers yields approximately the same results (38.3% of constrained variation, 42.7% for the first axis, and 15.4% for the second axis). As the parameter orientation is not retained by the forward selection model and because it produces a poorly readable figure, we also tested the same model without the orientation variable (Figure2). The results are the following: 28.3% of constrained variation, 55.9% for the first axis, and 17.7% for the second axis.Figure 2
Plot of the tb-RDA analysis with (a) and without (b) orientation variable.
(a) (b)The RDA analysis is potted both with and without the orientation variable (Figure2). The scaling 2 option [37] allows the following comments and shows that slope, NO3, NH4, PO4, and canopy cover play an important role in the dispersion of the sites along the first axis.The two variables NH4 and PO4 are very closely correlated, suggesting that they can act in a similar way on the species assemblages and between factors themselves; however, NO3 is negatively correlated with both NH4 and PO4. The two characteristic speciesEucladium verticillatum andPalustriella commutata are close together suggesting they appear both on sites with higher slope and lower cover of canopy and lower NH4 and PO4 concentrations.Pellia endiviifolia shows a positive relationship with NO3 whereasBrachythecium rivulare andOxyrrhynchium hians (Hedw.) Loeske are weakly correlated with PO4, NH4, and high pH values.Bryum pseudotriquetrum is negatively correlated with canopy cover. Most other species are clustered together away from these extremes. They show mostly shorter projections, indicating that they are either present in most of the sites or related to intermediate ecological conditions; however, they could also be related to some variables on third or higher axis which is not displayed.The result of the forward model selection of the RDA retained a model with (sorted by decreasing importance) slope, Mg, Ca, and canopy cover (spe ~ slope + Mg + Ca + cover). The same analysis without the outliers (“Haumont5” and “Givry2”) generates a slightly different model with slope, canopy cover, Mg, and Ca as the most significant factors (spe ~ slope + cover + Mg + Ca).


## 3.3. Species Richness

The results show (Table3) that only two explanatory variables (canopy cover and PO4) are strongly supported by the data (variable weight = 0.992 and 0.960, resp.) while two other ones are moderately supported by the data (NH4 and Mg) with a weight of, respectively, 0.709 and 0.650. All but the Mg variables are negatively correlated with species richness.Table 3
Results of the AIC stepwise model selection for species richness showing the best significant variables selected based on their weight using the complete dataset (columns on the left) or the data without 2 possible outliers (columns on the right).
Variable Weight of the variable Averaged coefficient Unconditional standard error Full model Model without possible outliers Full model Model without possible outliers Full model Model without possible outliers Cover 0.992 0.992 −0.007 −0.006 0.002 0.002 PO4 0.960 0.983 −7.510 −8.361 2.572 2.479 NH4 0.709 0.300 −5.615 −1.689 4.140 2.414 Mg 0.650 0.548 0.009 0.007 0.006 0.005 pH_mean 0.587 0.616 −0.186 −0.200 0.128 0.131 Zonecont 0.309 0.300 0.065 0.058 0.086 0.080 Ca 0.300 0.269 0.001 0.000 0.001 0.001 NO3 0.274 0.276 0.004 0.004 0.006 0.006 NO2 0.230 0.229 −0.002 −0.001 0.014 0.013 Slope moderate 0.087 0.090 0.007 0.008 0.018 0.019 Slope steep 0.087 0.09 0.005 0.008 0.021 0.023To check the potential influence of outliers we performed the same analysis after excluding Haumont5 and Givry2 sites which have very high PO4 and NH4 values. The model selection results are comparable to the first analysis including outliers; AIC retains only canopy cover and PO4 as the main explanatory variables (Table 3) (columns on the right for each parameter).Figures3 and 4 show the results of two predicted scenarios based on the complete model (2 outliers included) with the mean values of other variables. Figure 3 represents the relation between the PO4 concentration and the species richness and Figure 4 represents the relation between the canopy cover and the species richness. It can be seen that for the canopy cover the expected number of species rises by 2 or 3 species (going approximately from 2 to 4 or even 5) when the cover falls from 100 to 0%. In the case of the PO4 concentration the expected number of species falls to zero when the PO4 concentration rises from 0 to 0.5 mg·L−1 or more.Figure 3
Prediction of species richness in relation to PO4 concentration based on the coefficients of the best models selected by AIC stepwise selection.Figure 4
Prediction of species richness in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection.


## 3.4. Presence/Absence of Characteristic and Companion Species

For each species we began by excluding the variables that were highly correlated using the VIF (Variance Inflation Factor) values. The only variable to exclude from the five species was orientation and thus the binomial GLM analysis included the following explanatory variables: zone, NO2, Mg, pH_mean, Ca, NH4, NO3, canopy cover, slope, and PO4.


### 3.4.1.Palustriella commutata

The most important variables delivered by the AIC procedure are “steep slope” and “moderate slope” and to a lesser extend “canopy cover” (Table4). The frequency ofPalustriella commutata tends to increase when the slope is steeper and decreases when canopy cover is higher (Figure 5).Table 4
Results of the AIC stepwise model selection for probability of presence ofPalustriella commutata showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Moderate slope 0.902 1.116 1.002 Steep slope 0.902 3.152 1.010 Cover 0.797 −0.022 0.011 pH_mean 0.592 −1.293 0.947 NH4 0.387 −13.210 14.475 NO3 0.379 0.063 0.066 Ca 0.336 −0.006 0.007 PO4 0.283 1.874 3.610 NO2 0.254 −0.048 0.091 Mg 0.248 −0.004 0.014 Zonecont 0.243 0.0097 0.357Figure 5
Prediction of presence ofPalustriella commutata in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different slopes: low slope (solid line), moderate slope (dashed line), and steep slope (dotted line).


### 3.4.2.Eucladium verticillatum

The most important variables delivered by the AIC procedure are canopy cover and NH4, both are negatively correlated with the probability of the presence of the species (Table 5). The frequency ofEucladium verticillatum tends to decrease when the canopy cover increases and for higher concentrations of NH4, however the size of the effect is relatively limited as shown on Figure 6.Table 5
Results of the AIC stepwise model selection for probability of presence ofEucladium verticillatum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Canopy cover 0.931 −0.031 0.013 NH4 0.714 −80.324 52.086 PO4 0.523 −17.784 15.728 NO2 0.516 0.311 0.259 pH_mean 0.453 −0.684 0.625 Slope moderate 0.445 −0.256 0.675 Slope steep 0.445 0.862 0.772 Zonecont 0.383 6.353 878.235 NO3 0.277 −0.023 0.041 Mg 0.250 0.003 0.015 Ca 0.241 0.000 0.005Figure 6
Prediction of presence ofEucladium verticillatum in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different levels of NH4: maximum concentration in the dataset (dotted line), mean concentration in the dataset (dashed line), and minimum concentration in the dataset (solid line).


### 3.4.3.Cratoneuron filicinum

The only important variable delivered by the AIC procedure is the Mg concentration (positive relationship) and to a lesser degree NH4 (negative relationship) (Table 6, Figure 7). The results remain similar after excluding the “Laarbeek (Kwel)” site whose outlying value (65.5 mg·L−1) could have unduly influenced the correlation.Table 6
Results of the AIC stepwise model selection for probability of presence ofCratoneuron filicinum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Mg 0.986 0.127 0.049 NH4 0.734 −21.408 17.105 PO4 0.339 −2.269 3.217 Ca 0.324 −0.003 0.004 Cover 0.322 −0.002 0.003 pH_mean 0.261 −0.110 0.227 Zonecont 0.257 −0.019 0.231 NO2 0.239 0.012 0.049 NO3 0.238 0.005 0.022 Slope moderate 0.135 0.09 0.133 Slope steep 0.135 −0.018 0.121Figure 7
Prediction of presence ofCratoneuron filicinum in relation to Mg concentration based on the coefficients of the best models selected by AIC stepwise selection.


### 3.4.4.Pellia endiviifolia

The most important variables delivered by the AIC procedure are PO4 (negative relationship) and NO3 (positive relationship) (Table 7; Figure 8).Table 7
Results of the AIC stepwise model selection for probability of presence ofPellia endiviifolia showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error PO4 0.922 −28.250 13.310 NO3 0.885 0.251 0.113 Ca 0.650 −0.015 0.010 NO2 0.448 0.168 0.157 NH4 0.422 −4.857 5.898 Zonecont 0.419 −0.660 0.677 Cover 0.339 −0.003 0.004 Mg 0.257 0.004 0.010 pH_mean 0.240 −0.095 0.273 Slope moderate 0.233 0.252 0.269 Slope steep 0.233 0.296 0.319Figure 8
Prediction of presence ofPellia endiviifolia in relation to PO4 concentration based on the coefficients of the best models selected by AIC stepwise selection with the full dataset.


### 3.4.5.Brachythecium rivulare

The only important variable delivered by the AIC procedure is the Ca concentration (positive relationship) (Table8). However Figure 9 shows that this correlation is weak and is based almost entirely on the outlying value (251 mg·L−1) of the “Bois de Dieleghem” site. It would thus be sensible to not pay too much attention to this correlation.Table 8
Results of the AIC stepwise model selection for probability of presence ofBrachythecium rivulare showing the best significant variables selected based on their weight.
Variable Weight of the variable Averaged coefficient Unconditional standard error Ca 0.894 0.023 0.010 PO4 0.579 −9.225 7.229 Slope moderate 0.468 −0.193 0.369 Slope steep 0.468 −0.897 0.650 pH_mean 0.354 0.354 0.414 NH4 0.351 −7.123 10.000 Cover 0.292 0.002 0.003 Zonecont 0.285 −0.149 0.293 Mg 0.262 0.004 0.010 NO3 0.251 0.009 0.025 NO2 0.240 0.007 0.054Figure 9
Prediction of presence ofBrachythecium rivulare in relation to Ca concentration based on the coefficients of the best models selected by AIC stepwise selection.


## 3.4.1.Palustriella commutata

The most important variables delivered by the AIC procedure are “steep slope” and “moderate slope” and to a lesser extend “canopy cover” (Table4). The frequency ofPalustriella commutata tends to increase when the slope is steeper and decreases when canopy cover is higher (Figure 5).Table 4
Results of the AIC stepwise model selection for probability of presence ofPalustriella commutata showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Moderate slope 0.902 1.116 1.002 Steep slope 0.902 3.152 1.010 Cover 0.797 −0.022 0.011 pH_mean 0.592 −1.293 0.947 NH4 0.387 −13.210 14.475 NO3 0.379 0.063 0.066 Ca 0.336 −0.006 0.007 PO4 0.283 1.874 3.610 NO2 0.254 −0.048 0.091 Mg 0.248 −0.004 0.014 Zonecont 0.243 0.0097 0.357Figure 5
Prediction of presence ofPalustriella commutata in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different slopes: low slope (solid line), moderate slope (dashed line), and steep slope (dotted line).


## 3.4.2.Eucladium verticillatum

The most important variables delivered by the AIC procedure are canopy cover and NH4, both are negatively correlated with the probability of the presence of the species (Table 5). The frequency ofEucladium verticillatum tends to decrease when the canopy cover increases and for higher concentrations of NH4, however the size of the effect is relatively limited as shown on Figure 6.Table 5
Results of the AIC stepwise model selection for probability of presence ofEucladium verticillatum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Canopy cover 0.931 −0.031 0.013 NH4 0.714 −80.324 52.086 PO4 0.523 −17.784 15.728 NO2 0.516 0.311 0.259 pH_mean 0.453 −0.684 0.625 Slope moderate 0.445 −0.256 0.675 Slope steep 0.445 0.862 0.772 Zonecont 0.383 6.353 878.235 NO3 0.277 −0.023 0.041 Mg 0.250 0.003 0.015 Ca 0.241 0.000 0.005Figure 6
Prediction of presence ofEucladium verticillatum in relation to canopy cover based on the coefficients of the best models selected by AIC stepwise selection for different levels of NH4: maximum concentration in the dataset (dotted line), mean concentration in the dataset (dashed line), and minimum concentration in the dataset (solid line).


## 3.4.3.Cratoneuron filicinum

The only important variable delivered by the AIC procedure is the Mg concentration (positive relationship) and to a lesser degree NH4 (negative relationship) (Table 6, Figure 7). The results remain similar after excluding the “Laarbeek (Kwel)” site whose outlying value (65.5 mg·L−1) could have unduly influenced the correlation.Table 6
Results of the AIC stepwise model selection for probability of presence ofCratoneuron filicinum showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error Mg 0.986 0.127 0.049 NH4 0.734 −21.408 17.105 PO4 0.339 −2.269 3.217 Ca 0.324 −0.003 0.004 Cover 0.322 −0.002 0.003 pH_mean 0.261 −0.110 0.227 Zonecont 0.257 −0.019 0.231 NO2 0.239 0.012 0.049 NO3 0.238 0.005 0.022 Slope moderate 0.135 0.09 0.133 Slope steep 0.135 −0.018 0.121Figure 7
Prediction of presence ofCratoneuron filicinum in relation to Mg concentration based on the coefficients of the best models selected by AIC stepwise selection.


## 3.4.4.Pellia endiviifolia

The most important variables delivered by the AIC procedure are PO4 (negative relationship) and NO3 (positive relationship) (Table 7; Figure 8).Table 7
Results of the AIC stepwise model selection for probability of presence ofPellia endiviifolia showing the best significant variables selected based on their weight using the complete dataset.
Variable Weight of the variable Averaged coefficient Unconditional standard error PO4 0.922 −28.250 13.310 NO3 0.885 0.251 0.113 Ca 0.650 −0.015 0.010 NO2 0.448 0.168 0.157 NH4 0.422 −4.857 5.898 Zonecont 0.419 −0.660 0.677 Cover 0.339 −0.003 0.004 Mg 0.257 0.004 0.010 pH_mean 0.240 −0.095 0.273 Slope moderate 0.233 0.252 0.269 Slope steep 0.233 0.296 0.319Figure 8
Prediction of presence ofPellia endiviifolia in relation to PO4 concentration based on the coefficients of the best models selected by AIC stepwise selection with the full dataset.


## 3.4.5.Brachythecium rivulare

The only important variable delivered by the AIC procedure is the Ca concentration (positive relationship) (Table8). However Figure 9 shows that this correlation is weak and is based almost entirely on the outlying value (251 mg·L−1) of the “Bois de Dieleghem” site. It would thus be sensible to not pay too much attention to this correlation.Table 8
Results of the AIC stepwise model selection for probability of presence ofBrachythecium rivulare showing the best significant variables selected based on their weight.
Variable Weight of the variable Averaged coefficient Unconditional standard error Ca 0.894 0.023 0.010 PO4 0.579 −9.225 7.229 Slope moderate 0.468 −0.193 0.369 Slope steep 0.468 −0.897 0.650 pH_mean 0.354 0.354 0.414 NH4 0.351 −7.123 10.000 Cover 0.292 0.002 0.003 Zonecont 0.285 −0.149 0.293 Mg 0.262 0.004 0.010 NO3 0.251 0.009 0.025 NO2 0.240 0.007 0.054Figure 9
Prediction of presence ofBrachythecium rivulare in relation to Ca concentration based on the coefficients of the best models selected by AIC stepwise selection.


## 4. Discussion

This study aimed to identify the driving variables that explain the species richness and the distribution of the bryophyte assemblages within the 7220 habitat. The environmental driving factors discussed within this study apply only to the 7220 habitat of travertine forming springs and watercourses, and it would not be advisable to apply these findings outside of this habitat such as (less calcareous) types of water courses in Wallonia and Brussels-Capital Region. The springs and watercourses in this study are characterized by medium to high levels of Ca (60–300 mg·L−1) [40] and high pH values (7.5–8.5). The extreme values in our samples range from 76.5 to 251 mg·L−1 for Ca (the 27.8 value seeming doubtful) and pH from 6.2 to 8.2. In comparison Brusa and Cerabolini [11] recorded Ca values between 33.2 and 60.2 mg·L−1 and pH values between 7.5 and 8.4.


### 4.1. Global Analysis

The results of the tb-RDA showed that only 36.5% of the total variance in the dataset can be explained by the environmental variables, considered within this study. This suggests that other variables not measured here could explain better the observed species assemblages. Following other authors [41, 42], we suggest that factors such as the substrate itself (sand, loam, and rock), the micro topography [11, 16, 18, 33, 43], the water turbulence [2, 8, 44], the distance from the spring [8, 13], the presence and characteristics of travertine-productive microorganisms like cyanobacteria [8, 16, 45], and the local relative humidity could play an important role in the species assemblages even if they would probably be difficult to quantify.The results show that the most important variables explaining the rest of the variance are the slope, NO3, NH4, and PO4 concentrations (Figure 2). The slope has been identified by many authors as a key factor for the presence of the travertine communities [18] mainly because a steep slope generates more turbulence that enhances CO2 degassing and thus calcite precipitation that accounts for more than 80% of the precipitation in travertine [13, 45]. The negative relationship between the PO4 concentration in the spring water and the presence of mosses specific to petrifying sources should be further explored but could possibly be explained by the fact that PO4 has an inhibitive role in the formation of calcite [3, 46, 47]. Pentecost [13] also highlights the negative role of phosphate pollution on the deposits formation and on the travertine communities. These results are partly confirmed by the forward selection that has similarly retained slope and canopy cover but also Mg and Ca (as well as NH4 when the 2 possible outliers are dropped). That means that the relief, the canopy cover, and the degree of eutrophication play an important role among the variables. It is not surprising that NH4 and PO4 variables act in the same way (Figure 2). More curiously though is the opposite relation between these two variables and the NO3 concentration though a similar observation has been made by Denys and Oosterlynck [48] in Flanders. The second axis showed that the canopy cover also plays an important role in the dispersion of the species assemblages. This result is expected since we know that the exposure and the degree of relative humidity play a key role in the ecology of most of bryophytes species [49].The analysis also indicates that some species are more specifically linked to some variables considered in this study.Eucladium verticillatum andPalustriella commutata appear mostly in the same kind of locations characterized by a moderate or steep slope and with no or very low levels of eutrophication. This result is similar to analysis in Italy, using the same multivariate analysis [11]. Canopy cover is also negatively correlated with these sites indicating that these species occur more frequently at enlightened sites.Pellia endiviifolia shows a tendency to grow in NO3-rich sites, which is confirmed by other observation that the species can grow in oligotrophic and eutrophic waters [40]. FinallyBrachythecium rivulare andOxyrrhynchium hians are more likely to be found in more eutrophicated sites (determined by PO4 and NH4 values).


### 4.2. Species Richness

The Poisson GLM analysis and the AIC have retained two major significant variables concerning the species richness, that is, canopy cover and PO4 concentration. However other variables that have not been included in this analysis, such as soil type and water turbulence, should be considered in future studies. For example, Pentecost [13] has stated that two other variables, site size and heterogeneity, are also important factors affecting species richness. It can be seen in Figure 4 that all other registered variables being equal there could be a gain of only 2 or 3 species when clearing completely a theoretical site. This gain does not predict if the gained species would be characteristic species or other species.The negative relation with PO4, and with NH4 when keeping the outliers in the dataset, indicates that the more oligotrophic the conditions the more rich the community. Other studies indicate that this kind of relationship is not always attributed to eutrophication, and human impacts are sometimes positively correlated to species richness [48]. These studies may address a wider range of conditions than in the present study and suggest that this relationship is scale-dependent. The majority of the richest sites were characterized by PO4 concentration < 0.1 mg·L−1 which is very close to the 0.05 mg·L−1 threshold determined by Galoux et al. [41] for the reference conditions of Walloon watercourses. Brusa and Cerabolini [11] in Italy using multivariate analysis found that the 3 major factors explaining their species assemblages were the permanency/nonpermanency of the water flow, the slope, and the canopy cover. In this study the slope is not retained in the species richness analysis; however, it is an important variable forPalustriella commutata.


### 4.3. Characteristic and Companion Species

#### 4.3.1.Palustriella commutata

Our analysis shows that slope and to a lesser extend canopy cover are important variables explaining the presence ofPalustriella commutata in the surveyed sites. The absence of canopy combined with a steep slope increases the probability of presence ofPalustriella commutata (Figure 5). The slope has been detected as the only significant variable by Brusa and Cerabolini [11]. The fact that slope seems to play an important role in the probability of presence of the species seems contradictory with the well-known occurrence of the species in calcareous meadows in Belgium [16, 28]. It is possible that the properties of the water including highly mineralized or calcareous waters and permanently wet surfaces could be even more important than slope [11, 19, 28, 50]. It has been mentioned by many authors [13, 43, 51, 52] that there are two subspecies or species [14]Palustriella commutata andP. falcata whose ecological preferences are slightly different. According to these authorsP. commutata occurs relatively more often on boggy sites and around small springs and seepages with a higher pH and higher Ca concentrations, and it tolerates higher rates of nitrates and grows almost exclusively on limestone.P. falcata seems more restricted to open habitat of turbulent water at higher altitude and in less bases-rich waters. Concerning the canopy cover other authors [1, 13, 18, 50] mention the fact that this factor must play a role even if the species tolerates much shade. The species seems also to tolerate a certain amount of eutrophication [18, 34, 50] even if it grows mostly in oligotrophic circumstances.


#### 4.3.2.Eucladium verticillatum

Although canopy cover and NH4 are both negatively linked with the probability of presence of the species these variables seem not to play a major role. It is probable that other variables could also influence species richness including less permanently wet substrate by capillarity or because of being splashed. Indeed we found the species abundant around a spring on a steep sandy substrate in the Rouge-Cloître in the Brussels-Capital Region that is not splashed at all but is permanently wet due to capillarity and high relative humidity. The relative weak negative relationship with canopy cover is mentioned by some authors [2, 24, 50] that found the species in much shaded habitats.


#### 4.3.3.Cratoneuron filicinum

This species is not recognized in Wallonia as a characteristic species of the 7220 habitat as it is widely distributed [19], occupying calcareous meadows, calcareous springs, and wet rocks, as well as forest tracks where dolomite gravels have been added to harden the soil. In the Walloon watercoursesCratoneuron filicinumhas been identified as one of the few indicative species of more or less calcareous streams [41]. In our sites the species has a relatively wide ecological amplitude as regards canopy cover and light to moderate eutrophication. The tolerance to a certain level of eutrophication is also mentioned by Bailly et al. [50] and by Sossey-Alaoui and Rosillon [40]. Mg proves to be a key-element in our sites and this observation should be related to the fact that the species is especially common on dolomite substrates, such as forests tracks that are rich in Mg, as well as on marl substrates in Lorraine [19].


#### 4.3.4.Pellia endiviifolia

This is a widespread species in Wallonia and Brussels [19, 53] commonly associated with neutral to basic waters. It is also known to tolerate shade as well as moderately eutrophic waters [50]. It is therefore not surprising that none of these variables show clear relationships with the probability of presence in our sites. The apparent contradictory relationships with PO4 and NO3 are hard to clarify. Sossey-Alaoui and Rosillon [40] indicate that the species can be an indicator species both in oligotrophic streams and in nitrates-rich streams, providing that the waters are mineral-rich (high conductivity/high Ca concentration).


#### 4.3.5.Brachythecium rivulare

Together withPellia endiviifolia this species is relatively widespread in Wallonia and Brussels-Capital Region [19, 53]. It occurs along water courses and in marshes, though it is less linked to calcareous waters. In Wallonia it has been demonstrated that it has very wide ecological amplitude that is common in every natural region independent of the substrate [41]. It has also wide ecological amplitude occurring in a range of trophic levels and tolerating shade but preferring open sites [50]. This high ecological amplitude mirrors in the quasi-absence of significant variables explaining the presence of the species in our dataset.


### 4.4. Management Measures

One of the main objectives of this study was to define management objectives that could be implemented to maintain or improve the conservation status of the habitat 7220. As only relatively small proportion of the observed variability can be explained by the surveyed variables; the inclusion of further variables is recommended for future studies. Additionally among the surveyed variables some cannot be managed at all like the slope and Mg concentrations. Nevertheless, our results provide information on key variables that should be considered when managing these sites. The management measures may be different depending on their intention and may vary if the aim is to enhance biodiversity or favouring particular species. Generally speaking, we have seen that one of the most prominent results is that the species assemblage characteristic of the 7220 habitat (Palustriella commutata andEucladium verticillatum) favours very low PO4 concentration (PO4 < 0.2 mg·L−1) and low canopy cover. This is also the case but to a lesser degree with NH4. Graham and Farr [34] have stated that these two species are also mostly present when PO4 concentration is less than 0.05 mg·L−1.Brachythecium rivulare,Oxyrrhynchium hians, and evenPellia endiviifolia can occur in eutrophic conditions; however, in these conditions there is a lower probability of presence of the two characteristic species.The more open the site the more diverse the moss assemblages will be even if the expected number of additional species is only 2 or 3 (Figure4); however, the favoured species would not automatically be characteristic species of the 7220 habitat. If canopy cover plays a role in favouring species richness, it would be advisable not to cut down all existing trees and bushes but to undertake thinning to avoid possible radical negative changes in relative humidity and temperature on the bryophyte communities.We recommend that regular monitoring of the water chemistry is the best way to detect abnormal changes especially eutrophication (PO4, NH4, NO3). Monitoring regimes are already in place for most of the Walloon and Brussels sites but should be extended to all 7220 known sites.


### 4.5. Suggestions for Future Studies

To try to intercept other significant factors in the distribution and the presence/absence of the most characteristic species of the 7220 habitat, further studies should seek to take into account the following variables: type of substrate (sandy, calcareous, silty, etc.), the micro topography, the water turbulence, the distance from the spring, the relative humidity, and the presence of algae/cyanobacteria.


## 4.1. Global Analysis

The results of the tb-RDA showed that only 36.5% of the total variance in the dataset can be explained by the environmental variables, considered within this study. This suggests that other variables not measured here could explain better the observed species assemblages. Following other authors [41, 42], we suggest that factors such as the substrate itself (sand, loam, and rock), the micro topography [11, 16, 18, 33, 43], the water turbulence [2, 8, 44], the distance from the spring [8, 13], the presence and characteristics of travertine-productive microorganisms like cyanobacteria [8, 16, 45], and the local relative humidity could play an important role in the species assemblages even if they would probably be difficult to quantify.The results show that the most important variables explaining the rest of the variance are the slope, NO3, NH4, and PO4 concentrations (Figure 2). The slope has been identified by many authors as a key factor for the presence of the travertine communities [18] mainly because a steep slope generates more turbulence that enhances CO2 degassing and thus calcite precipitation that accounts for more than 80% of the precipitation in travertine [13, 45]. The negative relationship between the PO4 concentration in the spring water and the presence of mosses specific to petrifying sources should be further explored but could possibly be explained by the fact that PO4 has an inhibitive role in the formation of calcite [3, 46, 47]. Pentecost [13] also highlights the negative role of phosphate pollution on the deposits formation and on the travertine communities. These results are partly confirmed by the forward selection that has similarly retained slope and canopy cover but also Mg and Ca (as well as NH4 when the 2 possible outliers are dropped). That means that the relief, the canopy cover, and the degree of eutrophication play an important role among the variables. It is not surprising that NH4 and PO4 variables act in the same way (Figure 2). More curiously though is the opposite relation between these two variables and the NO3 concentration though a similar observation has been made by Denys and Oosterlynck [48] in Flanders. The second axis showed that the canopy cover also plays an important role in the dispersion of the species assemblages. This result is expected since we know that the exposure and the degree of relative humidity play a key role in the ecology of most of bryophytes species [49].The analysis also indicates that some species are more specifically linked to some variables considered in this study.Eucladium verticillatum andPalustriella commutata appear mostly in the same kind of locations characterized by a moderate or steep slope and with no or very low levels of eutrophication. This result is similar to analysis in Italy, using the same multivariate analysis [11]. Canopy cover is also negatively correlated with these sites indicating that these species occur more frequently at enlightened sites.Pellia endiviifolia shows a tendency to grow in NO3-rich sites, which is confirmed by other observation that the species can grow in oligotrophic and eutrophic waters [40]. FinallyBrachythecium rivulare andOxyrrhynchium hians are more likely to be found in more eutrophicated sites (determined by PO4 and NH4 values).


## 4.2. Species Richness

The Poisson GLM analysis and the AIC have retained two major significant variables concerning the species richness, that is, canopy cover and PO4 concentration. However other variables that have not been included in this analysis, such as soil type and water turbulence, should be considered in future studies. For example, Pentecost [13] has stated that two other variables, site size and heterogeneity, are also important factors affecting species richness. It can be seen in Figure 4 that all other registered variables being equal there could be a gain of only 2 or 3 species when clearing completely a theoretical site. This gain does not predict if the gained species would be characteristic species or other species.The negative relation with PO4, and with NH4 when keeping the outliers in the dataset, indicates that the more oligotrophic the conditions the more rich the community. Other studies indicate that this kind of relationship is not always attributed to eutrophication, and human impacts are sometimes positively correlated to species richness [48]. These studies may address a wider range of conditions than in the present study and suggest that this relationship is scale-dependent. The majority of the richest sites were characterized by PO4 concentration < 0.1 mg·L−1 which is very close to the 0.05 mg·L−1 threshold determined by Galoux et al. [41] for the reference conditions of Walloon watercourses. Brusa and Cerabolini [11] in Italy using multivariate analysis found that the 3 major factors explaining their species assemblages were the permanency/nonpermanency of the water flow, the slope, and the canopy cover. In this study the slope is not retained in the species richness analysis; however, it is an important variable forPalustriella commutata.


## 4.3. Characteristic and Companion Species

### 4.3.1.Palustriella commutata

Our analysis shows that slope and to a lesser extend canopy cover are important variables explaining the presence ofPalustriella commutata in the surveyed sites. The absence of canopy combined with a steep slope increases the probability of presence ofPalustriella commutata (Figure 5). The slope has been detected as the only significant variable by Brusa and Cerabolini [11]. The fact that slope seems to play an important role in the probability of presence of the species seems contradictory with the well-known occurrence of the species in calcareous meadows in Belgium [16, 28]. It is possible that the properties of the water including highly mineralized or calcareous waters and permanently wet surfaces could be even more important than slope [11, 19, 28, 50]. It has been mentioned by many authors [13, 43, 51, 52] that there are two subspecies or species [14]Palustriella commutata andP. falcata whose ecological preferences are slightly different. According to these authorsP. commutata occurs relatively more often on boggy sites and around small springs and seepages with a higher pH and higher Ca concentrations, and it tolerates higher rates of nitrates and grows almost exclusively on limestone.P. falcata seems more restricted to open habitat of turbulent water at higher altitude and in less bases-rich waters. Concerning the canopy cover other authors [1, 13, 18, 50] mention the fact that this factor must play a role even if the species tolerates much shade. The species seems also to tolerate a certain amount of eutrophication [18, 34, 50] even if it grows mostly in oligotrophic circumstances.


### 4.3.2.Eucladium verticillatum

Although canopy cover and NH4 are both negatively linked with the probability of presence of the species these variables seem not to play a major role. It is probable that other variables could also influence species richness including less permanently wet substrate by capillarity or because of being splashed. Indeed we found the species abundant around a spring on a steep sandy substrate in the Rouge-Cloître in the Brussels-Capital Region that is not splashed at all but is permanently wet due to capillarity and high relative humidity. The relative weak negative relationship with canopy cover is mentioned by some authors [2, 24, 50] that found the species in much shaded habitats.


### 4.3.3.Cratoneuron filicinum

This species is not recognized in Wallonia as a characteristic species of the 7220 habitat as it is widely distributed [19], occupying calcareous meadows, calcareous springs, and wet rocks, as well as forest tracks where dolomite gravels have been added to harden the soil. In the Walloon watercoursesCratoneuron filicinumhas been identified as one of the few indicative species of more or less calcareous streams [41]. In our sites the species has a relatively wide ecological amplitude as regards canopy cover and light to moderate eutrophication. The tolerance to a certain level of eutrophication is also mentioned by Bailly et al. [50] and by Sossey-Alaoui and Rosillon [40]. Mg proves to be a key-element in our sites and this observation should be related to the fact that the species is especially common on dolomite substrates, such as forests tracks that are rich in Mg, as well as on marl substrates in Lorraine [19].


### 4.3.4.Pellia endiviifolia

This is a widespread species in Wallonia and Brussels [19, 53] commonly associated with neutral to basic waters. It is also known to tolerate shade as well as moderately eutrophic waters [50]. It is therefore not surprising that none of these variables show clear relationships with the probability of presence in our sites. The apparent contradictory relationships with PO4 and NO3 are hard to clarify. Sossey-Alaoui and Rosillon [40] indicate that the species can be an indicator species both in oligotrophic streams and in nitrates-rich streams, providing that the waters are mineral-rich (high conductivity/high Ca concentration).


### 4.3.5.Brachythecium rivulare

Together withPellia endiviifolia this species is relatively widespread in Wallonia and Brussels-Capital Region [19, 53]. It occurs along water courses and in marshes, though it is less linked to calcareous waters. In Wallonia it has been demonstrated that it has very wide ecological amplitude that is common in every natural region independent of the substrate [41]. It has also wide ecological amplitude occurring in a range of trophic levels and tolerating shade but preferring open sites [50]. This high ecological amplitude mirrors in the quasi-absence of significant variables explaining the presence of the species in our dataset.


## 4.3.1.Palustriella commutata

Our analysis shows that slope and to a lesser extend canopy cover are important variables explaining the presence ofPalustriella commutata in the surveyed sites. The absence of canopy combined with a steep slope increases the probability of presence ofPalustriella commutata (Figure 5). The slope has been detected as the only significant variable by Brusa and Cerabolini [11]. The fact that slope seems to play an important role in the probability of presence of the species seems contradictory with the well-known occurrence of the species in calcareous meadows in Belgium [16, 28]. It is possible that the properties of the water including highly mineralized or calcareous waters and permanently wet surfaces could be even more important than slope [11, 19, 28, 50]. It has been mentioned by many authors [13, 43, 51, 52] that there are two subspecies or species [14]Palustriella commutata andP. falcata whose ecological preferences are slightly different. According to these authorsP. commutata occurs relatively more often on boggy sites and around small springs and seepages with a higher pH and higher Ca concentrations, and it tolerates higher rates of nitrates and grows almost exclusively on limestone.P. falcata seems more restricted to open habitat of turbulent water at higher altitude and in less bases-rich waters. Concerning the canopy cover other authors [1, 13, 18, 50] mention the fact that this factor must play a role even if the species tolerates much shade. The species seems also to tolerate a certain amount of eutrophication [18, 34, 50] even if it grows mostly in oligotrophic circumstances.


## 4.3.2.Eucladium verticillatum

Although canopy cover and NH4 are both negatively linked with the probability of presence of the species these variables seem not to play a major role. It is probable that other variables could also influence species richness including less permanently wet substrate by capillarity or because of being splashed. Indeed we found the species abundant around a spring on a steep sandy substrate in the Rouge-Cloître in the Brussels-Capital Region that is not splashed at all but is permanently wet due to capillarity and high relative humidity. The relative weak negative relationship with canopy cover is mentioned by some authors [2, 24, 50] that found the species in much shaded habitats.


## 4.3.3.Cratoneuron filicinum

This species is not recognized in Wallonia as a characteristic species of the 7220 habitat as it is widely distributed [19], occupying calcareous meadows, calcareous springs, and wet rocks, as well as forest tracks where dolomite gravels have been added to harden the soil. In the Walloon watercoursesCratoneuron filicinumhas been identified as one of the few indicative species of more or less calcareous streams [41]. In our sites the species has a relatively wide ecological amplitude as regards canopy cover and light to moderate eutrophication. The tolerance to a certain level of eutrophication is also mentioned by Bailly et al. [50] and by Sossey-Alaoui and Rosillon [40]. Mg proves to be a key-element in our sites and this observation should be related to the fact that the species is especially common on dolomite substrates, such as forests tracks that are rich in Mg, as well as on marl substrates in Lorraine [19].


## 4.3.4.Pellia endiviifolia

This is a widespread species in Wallonia and Brussels [19, 53] commonly associated with neutral to basic waters. It is also known to tolerate shade as well as moderately eutrophic waters [50]. It is therefore not surprising that none of these variables show clear relationships with the probability of presence in our sites. The apparent contradictory relationships with PO4 and NO3 are hard to clarify. Sossey-Alaoui and Rosillon [40] indicate that the species can be an indicator species both in oligotrophic streams and in nitrates-rich streams, providing that the waters are mineral-rich (high conductivity/high Ca concentration).


## 4.3.5.Brachythecium rivulare

Together withPellia endiviifolia this species is relatively widespread in Wallonia and Brussels-Capital Region [19, 53]. It occurs along water courses and in marshes, though it is less linked to calcareous waters. In Wallonia it has been demonstrated that it has very wide ecological amplitude that is common in every natural region independent of the substrate [41]. It has also wide ecological amplitude occurring in a range of trophic levels and tolerating shade but preferring open sites [50]. This high ecological amplitude mirrors in the quasi-absence of significant variables explaining the presence of the species in our dataset.


## 4.4. Management Measures

One of the main objectives of this study was to define management objectives that could be implemented to maintain or improve the conservation status of the habitat 7220. As only relatively small proportion of the observed variability can be explained by the surveyed variables; the inclusion of further variables is recommended for future studies. Additionally among the surveyed variables some cannot be managed at all like the slope and Mg concentrations. Nevertheless, our results provide information on key variables that should be considered when managing these sites. The management measures may be different depending on their intention and may vary if the aim is to enhance biodiversity or favouring particular species. Generally speaking, we have seen that one of the most prominent results is that the species assemblage characteristic of the 7220 habitat (Palustriella commutata andEucladium verticillatum) favours very low PO4 concentration (PO4 < 0.2 mg·L−1) and low canopy cover. This is also the case but to a lesser degree with NH4. Graham and Farr [34] have stated that these two species are also mostly present when PO4 concentration is less than 0.05 mg·L−1.Brachythecium rivulare,Oxyrrhynchium hians, and evenPellia endiviifolia can occur in eutrophic conditions; however, in these conditions there is a lower probability of presence of the two characteristic species.The more open the site the more diverse the moss assemblages will be even if the expected number of additional species is only 2 or 3 (Figure4); however, the favoured species would not automatically be characteristic species of the 7220 habitat. If canopy cover plays a role in favouring species richness, it would be advisable not to cut down all existing trees and bushes but to undertake thinning to avoid possible radical negative changes in relative humidity and temperature on the bryophyte communities.We recommend that regular monitoring of the water chemistry is the best way to detect abnormal changes especially eutrophication (PO4, NH4, NO3). Monitoring regimes are already in place for most of the Walloon and Brussels sites but should be extended to all 7220 known sites.


## 4.5. Suggestions for Future Studies

To try to intercept other significant factors in the distribution and the presence/absence of the most characteristic species of the 7220 habitat, further studies should seek to take into account the following variables: type of substrate (sandy, calcareous, silty, etc.), the micro topography, the water turbulence, the distance from the spring, the relative humidity, and the presence of algae/cyanobacteria.


## 5. Conclusions

In this study, we highlighted the variables explaining the species richness and the distribution of moss assemblages of the 7220 habitat in the southern part of Belgium. The driving factors highlighted in this study can only be applied to the 7220 habitat and would probably not be valuable for studies at sites dominated by less calcareous waters in Wallonia and Brussels-Capital Region. Our study recommends that monitoring of water chemistry (eutrophication) should be applied to all 7220 habitats, and this information should be used to support practical management actions to maintain or achieve a good status of conservation.



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*Source: 5365412-2016-11-15.xml*