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To link to this article : DOI:10.1007/s10980-013-9892-y URL : http://dx.doi.org/10.1007/s10980-013-9892-y

To cite this version : Bernadou, Abel and Céréghino, Régis and Barcet, Hugues and Combe, Maud and Espadaler, Xavier and Fourcassié, Vincent. Physical and land-cover variables influence ant functional groups and species diversity along elevational gradients. (2013) Landscape Ecology, vol. 28 (n° 7). pp. 1387-1400. ISSN 0921-2973

Any correspondance concerning this service should be sent to the repository administrator: [email protected] Physical and land-cover variables influence ant functional groups and species diversity along elevational gradients

Abel Bernadou · Régis Céréghino · Hugues Barcet · Maud Combe · Xavier Espadaler · Vincent Fourcassié

Abstract Of particular importance in shaping spe­ characterize the relationship between the spatial cies assemblages is the spatial heterogeneity of the distribution of ant functional groups, species diversity, environment. The aim of our study was to investigate and the variables measured. The use of SOM allowed the influence of spatial heterogeneity and environ­ us to reduce the apparent complexity of the environ­ mental complexity on the distribution of ant functional ment to five clusters that highlighted two main groups and species diversity along altitudinal gradi­ gradients: an altitudinal gradient and a gradient of ents in a temperate ecosystem (Pyrenees Mountains). environmental closure. The composition of ant func­ During three summers, we sampled 20 sites distributed tional groups and species diversity changed along both across two Pyrenean valleys ranging in altitude from of these gradients and was differently affected by 1,009 to 2,339 rn by using pitfall traps and band environmental variables. The SOM also allowed us to collection. The environment around each sampling validate the contours of most ant functional groups by points was characterized by using both physical and highlighting the response of these groups to the land-cover variables. We then used a self-organizing environmental and land-cover variables. map algorithm (SOM, neural network) to detect and Keywords Ants · Community ecology · Elevation gradient · Landscape heterogeneity ·Neural networks · Pyrenees

A. Bemadou (r:gJ) ·M. Combe · V. Fourcassié H. Barcet Centre de Recherches sur la Cognition Animale, UPS, UMR 5602 CNRS, Maison de la Recherche du Mirail, CNRS, Université de Toulouse, 118 route de Narbonne, Geode, Université Toulouse II-Le Mirail, 5 Allées 31062 Toulouse cedex 9, France A Machado, 31058 Toulouse, France e-mail: [email protected] X. Espadaler Present Address: Departament de Biologia Animal, de Biologia Vegetal A. Bemadou i d'Ecologia, Facultat de Ciències, Universitat Autônoma Evolution, Behaviour & Genetics-Biology 1, University de Barcelona, 08193 Bellaterra, Spain of Regensburg, UniversitiitsstraBe 31, 93053 Regensburg, Germany

R. Céréghino EcoLab, Université Paul Sabatier, Batiment 4R1, 118 Route de Narbonne, 31062 Toulouse cedex 4, France Introduction Andersen 2003), South (Bestelmeyer and Wiens 1996) and North America (Andersen 1997a; Stephens One of the main concerns in community ecology is to and Wagner 2006), and Asia (Pfeiffer et al. 2003), this identify the environmental factors (either biotic or method has been rarely used to study the ant fauna of abiotic) that shape species assemblages (Rosenzweig Europe (but see G6mez et al. 2003). 1995). Of particular importance in this respect is the Environmental heterogeneity may act at multiple heterogeneity created by the variation of these factors. scales on animais, both spatially and temporally According to the hypothesis of habitat heterogeneity (Wiens 1989; Levin 1992). All ofthese scales however suggested by MacArthur and MacArthur (1961), may not be relevant to understand how an animal species richness should increase with increasing interacts with its environment and the choice of the structural complexity of the environment. This rela­ spatial scale at which to study environmental hetero­ tionship has indeed been found in many taxa, e.g. geneity should be consistent with its perception of the arthropods, birds, mammals, amphibians or reptiles environment. This requires the selection of appropri­ (see Tews et al. 2004 for a review). Environmental ate descriptive variables (Turner et al. 2001 ). Physical, heterogeneity can significantly influence not only chemical and biological data, however, are often species richness but also their relative distribution. difficult to analyze in an integrated way because they The distribution of ants for example is significantly are complex, noisy, and vary and covary in a non­ affected by the spatial heterogeneity generated by fire linear way (Lek and Guégan 2000). One solution is to (Parr and Andersen 2008), anthropogenic disturbances use modeling techniques, such as artificial neural (Kalif et al. 2001 ), habitat fragmentation (Vasconcelos networks, that are able to take into account the et al. 2006), or grazing (Bestelmeyer and Wiens 1996). complex structure of multi-dimensional datasets Natural gradients (e.g. altitude, latitude) are also a (Chon 2011 ). For example, the Self-Organizing Map major source of spatial heterogeneity that can influ­ algorithm (SOM, unsupervised neural network, Ko­ ence the structure of species assemblages. Mountain­ honen 2001) is a powerful and well-suited tool to ous areas in particular are characterized by rapid detect patterns in animal communities in relation to changes in climate, soil, or vegetation, over relatively environmental variables (Lek and Guégan 2000). short distances (Korner 2007). They thus offer SOMs have been used in ecology to study mostly considerable landscape heterogeneity on a condensed aquatic insect or fish communities (e.g. Compin and area and are ideal for exploring the ecological Céréghino 2007; ants: Groc et al. 2007; Delabie et al. mechanisms underlying spatial patterns in species 2009; Céréghino et al. 2010). In this study we used richness and distribution. SOM to fulfill two main objectives: (1) to describe In this study, we investigated the influence of landscape spatial patterns along altitudinal gradients spatial heterogeneity and environmental complexity and to explore whether the apparent complexity of along altitudinal gradients on the distribution of ant mountain environments can be reduced to a few functional groups and species diversity across two simple elements and (2) to address the question ofhow Pyrenean valleys: one located in Andorra, on the ant functional groups and pattern of species diversity Southern side of the Pyrenees (the Madriu-Perafita­ respond to the changes in land-cover and physical Claror valley), and another located in France, on the variables along these gradients. Northern side of the Pyrenees (the Pique valley). The categorization of organisms into functional groups has been widely used in the study of animal communities Methods (birds: Cody 1985; reptiles: Piank:a 1986). Species classification by functional groups reduces the appar­ Study area and sampling sites ent complexity of animal communities (Andersen 1997a) and thus facilitates the understanding of the Our study area was located in the Pyrenees, a general principles that govern the functioning of mountain range located in south-west Europe and that ecosystems. Although the classification in functional is shared between Spain, France and the Principality of groups has been used to study the ant fauna Andorra. Because of their orientation and geographie of Australia (Andersen 1995; Hoffmann and location, these mountains present considerable climatic contrasts. The northem and western sides of Sampling methods and species identification the Pyrenees have an oceanic climate, with rainfall throughout the year, mild winters and cool summers. At each of the 20 sites, we used a variation of the ALL The southem side in contrast has a more continental protocol (Agosti et al. 2000) to sample the ants. A climate, characterized by high solar radiation, torren­ 190 rn long line transect was traced and sampling tial rains at equinoxes, large temperature variations, points were placed on this line every 10 rn (mak:ing a and very cold winters and dry summers. total of 20 sampling points per site, yielding a total400 Two valleys were sampled in this study: the sampling points for the two valleys). The position of Madriu-Perafita-Claror, in Andorra, and the Pique the sampling points were recorded by means of a GPS valley, in France. The Madriu-Perafita-Claror valley is (Garmin® eTrex®) and subsequently loaded into a glacial valley located in the southeast part of DNA-GIS, a free geographie information system Andorra that covers an area of 4,247 ha. The valley (www .diva-gis.org). is oriented along an east-west axis and extends along Two collection methods were used to sample the an altitudinal gradient ranging from 1,055 to 2,905 m. ants at each sampling point: pitfall traps and hand The valley is well preserved: the production of timber collection. The pitfall traps consisted of plastic cups has ceased in the 1950s' and since the 1980s' there has (diameter: 35 mm, height: 70 mm), filled to one-third been almost no human intervention. Because of its of their height with ethylene glycol. The cups were state of preservation, the Madriu-Perafita-Claror val­ buried so that their upper lip was flushed with the ley has been registered in 2004 as W orld Heritage by surface of the substrate. The pitfall traps were therefore UNESCO for its culturallandscape (www.unesco.org, set in action immediately and were left in place for see Madriu-Perafita-Claror valley). The Pique valley 5-8 days (Table S 1). The pitfalls could not be operated is a glacial valley predominantly oriented along a for the same length of time because access to sorne of north-south axis, extending along an altitudinal gra­ the transects was difficult and was sometimes pre­ dient ranging from 650 to 3,116 m. lt is dominated by vented by adverse meteorological conditions. Pitfall peak:s over 3,000 rn in altitude that lie on the border trapping was supplemented by hand collecting around between France and Spain. This valley is part of the each sampling point at the moment the pitfalls were Natura 2,000 sites (www.natura2000.fr/); it covers an removed. Hand collecting consisted of one persan (the area of 8,251 ha divided into two main valleys (the same persan for ali transects) picking up ali visible ants Pique valley and the Lys valley). within a 2 rn radius around each trap during a We sampled ants at 20 sites (9 sites in the Madriu maximum of 3 min. Ants were searched on the ground valley and 11 sites in the Pique valley) in July-August and in the vegetation; potential nesting sites were also 2005 to 2007. To select the sampling sites, three main inspected (dead wood, undemeath stones/bark). The factors were considered: elevation, exposure, and type combination of pitfall and hand collecting sampling of vegetation cover. We sampled along an altitudinal techniques is known to perform well in temperate gradient ranging from 1,300 to 2,300 rn for the Madriu regions (Groc et al. 2007). Winkler extractors were not valley, and from 1,000 to 2,300 rn for the Pique valley. used because the leaf litter is generally shallow Sampling could not be achieved over a larger altitu­ (because of heavy rainfall, the presence of rocks, and dinal gradient, because of high anthropogenic pres­ high slope inclination) or relatively poor (particularly sures below 1,300 rn in the Madriu valley, and below in coniferous forests) in mountainous environments. 1,000 rn in the Pique valley. Locations higher than Ali ants collected at each sampling point were placed in 2,300 rn were not sampled because ant species plastic vials filled with 90 % ethanol. Once in the richness beyond this altitude is known to be very laboratory, ants were identified to the species level low (Glaser 2006). The two valleys were thus sampled using available keys (Seifert 2007). on 62.5 and 61.4 %of their altitudinal range, for the Because ants are social insects, a single sample may Madriu and Pique valleys respectively. The different contain a high abundance of a rare species. Our categories of vegetation covers considered for the analyses are therefore based on the species occurrence selection of the sampling sites were: forest, meadow, in the samples rather than on the number of individ­ seree and bushes. Table S 1 gives the main character­ uals. A sampling point thus corresponds to the istics of the sampling sites for the two valleys. presence/absence of various species collected at a sampling site by a pitfall trap or by hand collection Australian and North American ants and on that used around the pitfall or by both sampling methods. by Bestelmeyer and Wiens (1996) for South American Consequently, the theoretical maximum of a species ants. Given that sorne genera (e.g. Formica andLasius in occurrence in a transect is 20. this study) are heterogeneous in terms of their ecology and behaviour, different functional groups sometimes Environmental variables and habitat share species of the same genus. The five following characterization functional groups were distinguished:

The 20 sites sampled and the micro-environment Opportunists (0) these are, in general, unspecial­ around each pitfall were characterized by using four ized species, whose distributions are strongly physical and eight land-caver variables. These 12 influenced by competition with other ants. Accord­ variables were chosen because they have been shawn ing to Andersen (2000), these species often span a to be consistently correlated with ant species richness large diversity of habitats. They predominate in in previous studies (for physical variables see for areas where stress or disturbance limit ant diver­ example: Kaspari et al. 2004; Sanders et al. 2007; sity and biomass and thus in which behavioural Dunn et al. 2009a). The physical variables considered dominance is low. In our study, this group is were: annual mean temperature (in °C), annual represented by species of the genus Formica and precipitation (in mm), elevation a.s.l (in rn) and slope. by species like Tapinoma erraticum and Tetramo­ Annual mean temperature and annual precipitation rium impurum. T. erraticum was not classified as a were obtained from two GIS data layers (30 arc­ Dominant Dolichoderinae because its societies are seconds) of the WorldClim 1.4 database (Hijmans small (Seifert 2007) with a much reduced worker et al. 2005), whereas elevation was recorded directly number compared, e.g., to the polydomous species in the field by a GPS. WorldClim computes temper­ T. nigerrimum. ature as a function of elevation, which means that ali - Social Parasites (SP): this group gathers species points of a transect where characterized by the same that are either temporary (e.g. Lasius mixtus) or temperature value in our study. The slope was permanent (e.g. Strongylognatus testaceus) social characterized locally around each pitfall by the same parasites. persan throughout the whole study using the following - Coarse Woody Debris Specialist (CWDS) this scale: 0 (null to gentle slope ), 1 (moderate slope), group is represented by two species: Camponotus 2 (strong slope). To describe the area surrounding the herculeanus and C. ligniperda. These species nest pitfalls, digital photographs centered on each pitfall in stumps or tree trunks. were taken. Then, on each photograph, we considered - Cold Climate Specialists/Shadow Habitats ( CCS/ an area of 1 m2 centered on the pitfall and used an SW): these species have their distributions cen­ image analysis software to delineate the outline of the tered on cold climate areas (Andersen 2000). They following land-caver variables within this area: shrub, are generally characteristic of habitats where the bare rock/pebbles, dead wood/stump, litter, grass and abundance of dominant dolichoderines is low bare soil. The percentage of area covered by each of (Andersen 2000). This group is mainly represented these elements was then determined. In addition, we in the Madriu and Pique valleys by the genera also noted the presence/absence of either a hardwood Formica, Lasius and Myrmica. The ants of the or coniferous canopy above each pitfall. genus Myrmica were classified as CCS/SW rather than Opportunists because this genus is mainly Ant functional groups present within mountainous, humid and grassy environments (Radchenko and Elmes 2010). AU ant species collected in the Madriu and Pique valle ys - Cryptics ( C) the se species are small to tiny species. were classified into five functional groups (see Table S2) This group is predominantly represented by according to the categorization proposed by Roig and myrmicines and ponerines that nest and forage Espadaler (2010). This latter is an adaptation for the within soil, litter and dead branches (Andersen lberian Peninsula and Balearic Islands of the classifica­ 2000). These ants are mainly present in forested tion used by Andersen (1995, 1997a, 2000) for habitats. In our study, this group is represented by the two genera Leptothorax and Temnothorax and (ii) Tuning phase (7,000 steps): during this phase, by one species of the Lasius genus: L. flavus. only the virtual samples adjacent to the BMU Leptothorax and Temnothorax species were are lightly modified. At the end of the training, included in the Cryptics functional group because the BMU is determined for each sample, and they have cryptic behaviour in the sense that they each sample is set in the corresponding hexagon forage singly, move slowly and "have little of the SOM map. Neighbouring samples on the interaction with other epigaeic ants" (Andersen grid are expected to represent adjacent clusters 1995). of samples. Consequently, sampling points appearing distant in the modelling space ( according to physical and land-cover variables) Data analysis represent expected differences among sampling points in real environmental characteristics. To estimate total ant species richness at the valley and The self-organizing map for this study consists of transect levels and to evaluate the completeness of our two layers of neurons connected by weights: an input samples, Chao2, a non parametric richness estimator, layer and an output layer. The input layer was was calculated with the program EstimateS 7.5.2 (100 composed of 12 neurons (one per variable) connected replicates) (Colwe11 2005). to the 400 sampling points. The output layer was We used the SOM Toolbox (version 2) for Matlab® composed of 98 neurons (see below) visualized as developed by the Laboratory of Information and hexagonal cells organized on an array of 14 rows by 7 Computer Science at the Helsinki University of columns (Fig. l a). The number of output neurons Technology (http://www .cis.hut.fi/projects/somtool (map size) is important to detect the deviation of the box/, see Vesanto et al. (1999) for practical instruc­ data. If the map size is too small, it might not exp lain tions). The SOM is an unsupervised learning procedure sorne important differences that should be detected which transforms a set of multidimensional data into a (Compin and Céréghino 2007). Conversely, if the map two dimensional map subject to a topological con­ size is too big, the differences are too small. We straint (see Kohonen 2001 for details). The data are followed the procedure described in Park et al. (2003) projected onto a rectangular grid composed of hexag­ and Céréghino and Park (2009): the network was onal cells, forming a map (Giraudel and Lek 2001 ). The trained with different map sizes (4-200 neurons) and SOM plots the similarities of the data by grouping we chose the optimum map size based on local similar data items together as follows: minimum values for quantization and topographie (i) Virtual samples (visualized here as hexagonal errors. Quantization error is the average distance cells) are initialized with random samples taken between each data vector and its BMU and, thus, from the input data set. measures map resolution. Topographie error repre­ (ii) The virtual samples are updated in an iterative sents the proportion of ali data vectors for which 1st way: (1) a sample unit is randomly chosen as and 2nd BMUs are not adjacent, and is used for the an input unit, (2) the Euclidean distance measurement of topology preservation The number of between this sample unit and every virtual 98 output neurons retained for this study fitted well the sample is computed, (3) the virtual sample heuristic rule suggested by Vesanto et al. (2000) who closest to the input unit is selected and called reported that the optimal number of map units is close 'best matching unit' (BMU), and (4) the BMU to 5-Jn, where n is the number of samples. For each and its neighbours are moved a bit towards the sampling point, we made a list of the different species input unit. collected and determined the values of the environ­ mental variables characterizing the sampling point. To The training is separated into two parts: highlight the relationships between the different ant (i) Ordering phase (the 3,000 first steps): when this functional groups and the environmental variables, the phase takes place, the samples are highly mod­ number of species occurrences of each functional ified in the wide neighbourhood of the BMU. group was introduced into the SOM previously trained MTIP15 Fig. 1 Distribution of the MT6P16 UTIP17 sampling points on the self­ MT6P1G "''""' organizing map (SOM). """"' A """"""""' """""""'' a The sampling points are .,.,,"""" """" .,.,, """" distributed according to four """" physical variables (altitude, slope, mean annual temperature and mean precipitation) and eight land-cover variables variables (presence of a hardwood or coniferous canopy, percent area covered by shrub, bare rock/ pebbles, dead wood/stump, litter, grass and bare soil in a 1 m2 area around each pitfall). Altitude, mean annual temperature and mean precipitation were obtained by using a GIS software while slope and the eight environmental variables were estimated directly in the field or by the analysis of digital photographs centered on each sampling points. The codes used to designate the sampling points on the SOM refer to the valley (M for Madriu, P for Pique), the transect number within the valley, and the location of the sampling points within the transect (e.g.: MT2P20, Madriu valley, Transect 2, sampling points 20). B Neighboring sampling points on the self-organizing -10,8 map share similar environmental -9,6 characteristics. b The SOM units were classified into -8,4 five clusters (A, B, C, D and E). The boundaries of the five clusters (A, B, C, D and >- -7,2 ·;::::: E) were obtained by J!!- applying Ward's algorithm ·e -6 to the weights of the i:i) variables in the SOM -4,8 hexagons. The smallest branches with numbers in -3,6 E D A B the dendrogram correspond c to the SOM neurons -2,4

-1,2 with the four physical and the eight land cover Results variables that characterize each sampling point. Dur­ ing the training of the map, we used a mask to give a Classification of sampling sites null weight to the five functional groups, whereas physical and land-cover variables were given a weight Mter training the Kohonen map with the four physical of 1. Therefore, the search for the BMU was based on variables and the eight land-cover variables, five the 4 physical and 8land-cover variables only. Setting clusters of sampling sites obtained from the SOM the mask value to zero for a given component (here for output were identified (Fig. l a, b). each of the five functional groups) removes the effect The SOM allowed us to identify two main gradients of that component on the map organization (Vesanto (Fig. 2 and Fig. S 1): a fust gradient extending from the et al. 2000). The values and distributions of the lower right to the upper left corner of the map (clusters functional groups were then visualized on the SOM D and E vs. cluster A, B and C), which corresponds to previously trained with physical and land-cover vari­ an altitudinal gradient ranging from low to high ables only and formed by the 98 hexagonal cells. altitudes, and a second gradient, extending from the In a last step, Ward's algorithm was used to identify bottom to the top of the map (clusters C, D andE vs. the boundaries between each cluster on the Kohonen cluster A and B), which corresponds to a gradient of map (Fig. 1b ). The distributions of the number of environmental closure, ranging from closed to open species occurrences in each ant functional groups in the areas. different clusters were compared using the x2 test for Cluster B corresponds to sampling sites of medium independent samples (Siegel and Castellan 1988). To elevations located in open areas, e.g. grassland areas further analyze the distribution of functional groups (Fig. 2). Cluster A is equivalent to cluster B but for within each cluster, an analysis of residuals was high elevation. It includes sampling sites typical of performed (Siegel and Castellan 1988). This analysis mountain environments, e.g. screes of high altitudes tests the contribution of each functional group to each located on steep slopes. A large proportion of the cluster. Moreover, it reveals whether a functional group sampling sites of cluster A is dominated by bare rocks is positively or negatively associated to a given cluster. and shrubs (Fig. 2). Clusters C and E correspond to We used a generalized linear mixed model sampling sites in forest areas: cluster E to low altitude (GLMM) with a Poisson error distribution to examine forests dominated by hardwood, with a high ahun­ the variation in ant species richness per sampling point dance of litter and dead wood, and cluster C to high among the SOM clusters. To account for spatial altitude forests in which conifers are predominant. autocorrelation among sampling points located in the Note that for cluster E the sampling sites with high same transects and in the same valley, the variable slopes are also characterized by bare soil. Cluster D transect was nested within the variable valley and was corresponds to a transition area between hardwood entered as a random variable in the model. To assess forests and grasslands (Fig. 2). the overall effect of the SOM clusters on species diversity we fitted a first model in which the SOM Distribution of ant functional groups and species cluster variable was entered as a fixed effect categor­ diversity ical factor and the transect variable (nested within valley) was entered as a random effect categorical In total, 42 ant species were found in the two valleys. factor. We then fitted a second model with no fixed The number of species collected at each transect effects and compared the two models with a likelihood varied between 25 at 1,351 rn and 2 at 2,339 rn, and ratio test (Zuur et al. 2009). The different SOM between 14 at 1,009 rn and 4 at 2,299 rn, for the clusters were then regrouped by removing non­ Madriu and Pique valley respectively. The Chao2 significant factor levels in a stepwise a posteriori estimator indicated that between 66 and 100 % procedure (Crawley 2007). The models were fitted (mean± SD = 93.48 ± 10.89) of the expected max­ with the statistical software R 2.11.0 (R Development imum number of species were collected for the 9 Core Team 2011) and the R-package lme4 (linear transects in the Madriu valley, and between 63 and mixed-effects models using S4 classes, Bates et al. 100 % (mean ± SD = 90.74 ± 12.29) for the 11 2011) using the function glmer. transects in the Pique valley using pitfall traps and Altitude (m a.s.l) Slope (from 0 to 2) Clusters

Hardwood canopy (0/1) Coniferous canopy (0/11 Litter (%1 Dead wood 1 stump (% 1 0.998 7.4

3.71

0 Grass (%1 Sare soil (%1 Shrub( ~·l Sare rock 1 pebbles ( ~. ) 47.5

24.2

0.935

Fil- l Gradient disaibution of each mVÛOIIIDeDtal variable 011 (clusteD A. B md C) altitudes, ami a gradient of enviroJJmcatal the 1raiDecl self-orgauizing map. A grayscale (datk = lligb closure. rangiDg from closed (cl.usters C, D aad E) to open value.ligbt = low values) was used to viBualize the value of the (clustcrs A and B) areas. See also Fig. Sl for the mean amrual variables. 1be SOM allows to derive two main gradients: an temperature IUid mean precipitation variables a11itudiDal gradient rangiDg from.low (clusten D andE) to higb band collecting. There was no relationship between 44-58 %, mean: 56 %, Fig. 4a). The Opportunists is these values and the duration of pitfall activity for the the second largest group (range: 23-47 %, mean: Madriu va11ey (Spearman's rank correlation: r = 30 %), followed. by the Cryptics (range: 8-19 %, -0.49, P = 0.17, n = 9) or the Pique valley (Spear­ mean: 11 %), the Coarse Woody Debris Specialists man's rank correlation: r = 0.15, P = 0.64, n = 11) (range: 1-15 %, mean: 5 %) and the Social Parasites (Table S1). (range: 0-3 %, mean: 1 %) (Figs. 3, 4a). The distribution of the :five functional groups on the The distribution of species occurrences m each SOM previously trained with the physical and land­ functional group was not homogeneous across the :five caver variables are shown mFig. 3. With the excep­ clusters (j- = 96.16, df = 16, p < 0.001). The tion of Social Parasites, ali functional groups are Coarse Woody Debris Specialists functional group present mthe :fi.ve clusters of the Kohonen map. The was signi:ficantly and positively associated with cluster Cold Climate Speci.alists/Shadow Habitats is the C (residuals = 6.2, Figs. 3, 4b) and negatively asso­ dominant functional group mmost clusters (range: ciated with cluster B {residual = -2.4, Figs. 3, 4b) Opportwlllta o.rr

0.1U

0

0

Fig. 3 Ant fuoctinnal groups. Visualization of the fi.ve ant be associated and to be fouml in the 111111e area. A grayscale functi.oDal groups on the sclf-m:ganizing map traiDed with the (ligbt = low values, dEk = bigh values) was used to visualize four physieal and the eigbt Jand.cover variables. Each furu:tional the levet of pre$eDCC of eacb furu:tional group. Note that the group bas its own disttilmtion pattern. The fnnctional groups grayscak1 are different for eaclt functional group oc:cupying llimilar zone& on the map have a high probability to

Atoo 8 B 8 ~10 .... :Il u Seo .1 i40 • ----~------~-- 110 t.

0 A 8 c D E Ali elu etant A Il c D E

Fig. 4 Analyail ofthe distribution ofthe ant func:tional groups. of the fi.ve clUiters idmtifi.ed on the sclf-organizing map. a Pm:entage of spccies DCC~~IIeDCes for earh functional group Acconling to the sigll of their residual values, the func:tional fouml in the Madriu and Pique valley. The pereeatagcs are givcu groups may be posilivcly or negativcly associated with the for eaclt of the fi.ve clusœrs idmtifi.ed by Ward's algoritbm on clustas. The two doUed. !iDes represent the significance the self-organizing map IIDCl for an clusterll grouped together. tbreshold at P = 0.05. 0 opportunim, SP social parasites, b Residual values for the five ant functional groups fOUDd in the CWDS coane woody debris spcciali&t, CCSISW cold climate Madriu and Pique valley. The residual values are given for each specialist8/8hadow habitats and C c::ryptics and toalesse.rextenttocluster A(residual = -1.8, not environmental variables characterizing the physical significant. Figs. 3. 4b). This functional group is thus environment and the type of land-cover around each characteristic of woodland areas and is negatively sampling point. The SOM algorithm :redu.ced the associated with open areas. The Cold Climate Spe­ complexity of the database to fi.ve clusters of sampling cialists/Shadow Habitat and Opporbmist functional points corresponding to high and low elevation groups do not show any clear distribution pattern. grassland areas, hardwood and coniferous forests, They are abundant in ali clusters (Fig. 4a). The Social and a transition area between hardwood forests and Parasites are signifi.cantly and positively associated grasslands. These clusters highligb.t two main gradi­ with cluster B (œsiduals = 3.8, Fig. 4b). The Cryptic ents: an altitudinal gradient that mimics to a certain group was significantly pœsent in cluster B (residu­ extent the altitudinal zonation of vegetation found in ais= 2.3. F1g. 4b) and is thus associated with the the Pyrenees (Ninot et al. 2007) and a gradient of presence of litter and han\wood canopy. environmental closure. We then categorized the The distribution of ant species ri.chness per sam­ species found at the sampling points into nve pling point differed signiftcantly among the five functional groups and used the SOM map generated clusters (GLMM, f = 94.18, df= 4, p < 0.001). by the algmithm to stud.y the distribution of these Cluster B bad the lowest ant species richness (mean ant groups in the environments sampled. Using this species richness per sampling point ±Cio.9.s: method, we were able to validate the contours of most 0.74 ± 0.27) while clusters B (3.77 ± 0.38) bad the functional groups by positively or negatively corre­ highest one (Fig. 5). Cluster D was only marginally lating their disttibution with the environmental vari­ significantly different from clusters A and C (GLMM, ables measmed. The disttibution of ant functional z value = 1.93, p = 0.052). groups changed along environmental gradients and was differently affec1ed by environmental variables. Finally. we examineA the disttibution of ant specles Dlscussion richness across the ôve clusters of sampling points identifi.ed by the SOM algorithm to fi.nd out the ln this study we used a Self-O.rganizing Map algorithm environmental characteristics associ.ated with low or to categorize 400 sampling points on the basia of 12 high ant species diversity. Three of the fi.ve functional groups (Coarse Woody Debris Specialists, Sociol 5 Parasites tmd the Cryptic) showed a clear pattern of a association with particular features ofthe environment ~ 4 such as the presence of litter and canopy. 1'hese tbree ü b groups however represented only 31 % of the 42 species collected whereas the disttibution pattern of the two other functional groups ( Cold Climate Specialist/Shadow Habitat and OpportrmistJJ) that m 2 represented 69 % of the total number of specles ·~ collected was much less clear. Our results show (/) c therefore that the disttibution of the ôve functional 8l 1 :2; groups we de1ined change along environm.ental gra­ dients. and that they are thus differently affected by 0 environmental variables. Assuming tbat species are A B c D E Ali clusters more likely to reach neighboring areas than areas far Clusters apart and that neighboring sampling points tend to Fig. 5 Ant speciea rlclmess. Mean 11UD1ber of ant speeies exhibit similar physical features, small-scale autocor­ (::1:: Clo-95) per sampling poim for eaclt SOM cluster (A-E) 8lld relatiom of ant assemblages were suggested from the for ail clustas groupcd togedler. Significant differences in ant SOM outputs (Figs. 1, 2, 3). However. almost ali ant speçi.cs ric:Jmess between clusteD wme tested with a genaalized functional groups (four out of fi.ve) were present linear mixed model (GLMM) with a Poisson eaor distribution. Different lettcrll above the error btJn indicate sigomcant within ali SOM clusters which shows that spatial differeDœs in mt species riclmess at P < 0.05 autoco.rrelation alone cannot explain the SOM oulputs. Indeed, if spatial autocorrelation were the only factor mound-building species of the genus Formica. At a explaining the relationship between sampling points local scale, these species are behaviorally dominant then each of the SOM elus ter would correspond to an throughout the Holarctic and they could thus be ant functional group. described as belonging to the dominant species Why does the SOM analysis show such a discrep­ functional group in local ant fauna (Andersen 1997b; ancy among functional groups in their pattern of Savolainen and VepsaHi.inen 1988). However, this association with environmental variables and why do dominance is limited to cool-temperate regions and at in particular both the Cold Climate SpecialistJShadow a global scale they would rather be considered as Habitat and Opportunists functional groups appear to belonging to the group of cold-climate specialists. The be so widely distributed in our sampling area? Two importance of competition and dominance in ant explanations could be provided. The fust explanation community structure is thus scale dependent. The could be that this result reflects true particular categorization in functional groups used in our study biological traits of the species belonging to these corresponds to that proposed by Roig and Espadaler groups. According to Andersen (1995, 1997a, 2000) (2010) to describe the ant fauna of the lberian the distribution of the Cold Climate Specialists/ Peninsula and Balearic Islands. Applied at the local Shadow Habitat species is centred on cool-temperate scale of our study area, this categorization may not be regions. In the two valleys we sampled, this group was discriminative enough (Andersen 1997b) and could represented by three genera: Formica, Lasius and conceal the response of sorne ant species to particular Myrmica. Most of the species of these genera are ecological variables. A solution could have been found holarctic and are characteristic of the cold regions of in subdividing sorne of the functional groups we used the northern hemisphere (Bernard 1968). Their suc­ (Bestelmeyer and Wiens 1996; Andersen 1997b). This cess in these regions is mainly due to specifie could have increased the discriminative power of our behavioural and/or physiological adaptive traits that analysis and as a consequence, clearer successional allow them to resist to low temperatures (Heinze 1992; patterns in relation to environmental variables could Maysov and Kipyatkov 2009). Since the whole area have been revealed. we sampled was located in a temperate mountainous The ant species belonging to the Coarse Woody region it should therefore come as no surprise that the Debris Specialists and Cryptic functional groups were Cold Climate Specialists/Shadow Habitat functional significantly and positively associated with woodland group was not found to be associated with any areas and negatively associated with open areas. The particular specifie environmental variable. As for the strong response of these two groups to the presence of species belonging to the Opportunistic functional a canopy can most likely be explained by their nesting group, we found that they occupy a wide range of habit. As a caveat however one should keep in mind habitat but were particularly present in grassland areas that SOM is a correlative analysis and thus does not of high altitudes, a relatively stressful environment for convey information on the mechanisms generating the ants, both because of the low temperatures, charac­ distributions observed. For example, we do not know teristic of high altitudes, and of the scarcity of food if the presence or absence of a species in a given (Andersen 2000). environment results from an effective choice of a A second and alternative explanation to the dis­ habitat by newly mated queens during colony found­ crepancy found among functional groups in their ing, from an impossibility to colonize a particular pattern of association with environmental variables environment, or whether it results from competition could be linked to the criteria used to define the mechanisms. For the Social Parasites, as for the functional groups. The criteria used to define the Cold Coarse Woody Debris Specialists and Cryptic groups, Climate SpecialistJShadow Habitat and Opportunists these species show a clear and localized pattern of functional groups could not be relevant to obtain clear distribution on the SOM. However, this result has to be patterns with the SOM analysis. As pointed out by interpreted cautiously. This functional group probably Andersen (1997b) the definition of functional groups does not respond to physical and land-caver variables is scale-dependent and one should thus be cautious in perse, but rather to the presence or absence of its hasts. using them in community ecolo gy studies. As a case in The mean number of species in the five clusters we point Andersen (1997b) gives the example of the identified ranged from O. 7 to 3. 7. Clusters B and D have a higher species richness compared to clusters A and C Along with cluster B, Cluster D was also one of the which have also the highest mean altitudes. The clusters characterized by a relatively high species decrease in ant species richness with increasing diversity. This is probably explained by the fact that it altitude has been reported in other studies (e.g. Sanders corresponds to transitional areas between hardwood et al. 2007, 2010; Lessard et al. 2007) and many forests and grassland. Ecotones are indeed known to hypotheses have been put forward to explain this have a positive effect on species richness (Risser 1995). pattern (see Dunn et al. 2009b ). Ants do not respond to An explanation for this is that an ecotone not only has its elevation per se; elevation is only a surrogate for a own characteristics (composition and structure) but also variety of factors that shape diversity gradients (Korner share the characteristics ofboth adjacent habitats (Risser 2007; Dunn et al. 2009b). Nevertheless we introduced 1995). Previous studies on "edge effect" howeverhave this environmental variable to examine how it is linked led to conflicting results (e.g. in insects: Dauber and to other habitat features (e.g. litter, shrubs, etc.). W olters 2004) and the results of the present study would Altitude at both of our field sites was correlated with thus need to be confirmed. steep slopes and bare rock areas, two environmental SOM have already been applied successfully on ants features that could negatively influence local ant to investigate the efficiency of sampling methods (Groc species richness by limiting potential nest sites. et al. 2007), the ecological impact of land use by A case point in the results is cluster E. Although it is Amerindians on ant diversity (Delabie et al. 2009), or characterized by a high structural complexity and a the impact of ant-plant mutualism on the diversity of low altitude it had the lowest species richness of ali invertebrate communities (Céréghino et al. 2010). We clusters. Habitat complexity is known to be an show here that the use of SOM canin addition be useful important factor driving species richness and commu­ to study the response of ant functional groups to nity composition in ants (Lassau and Hochuli 2004).1t environmental variables and land-cover features. This has generally been found that species richness corre­ technique can explore large and complex datasets and lates positively with the complexity of the environ­ thus can be used as an efficient tool in community ment (Andersen 1986; McCoy and Bell 1991 ). Our ecology to define the characteristics of the ecological results however do not seem to fit with this general niche of each species (Groc et al. 2007; Céréghino et al. observation. Clusters with greater ant diversity in our 2010). By using SOM we were able in addition to point study indeed were simple from a structural point of out the sites of greater ant biodiversity in our study area. view (see cluster B corresponding mainly to grassland Environmental variables were used to characterize the areas). A similar result has been found by Lassau and landscape around each sampling points. However, Hochuli (2004) and Lassau et al. (2005) in their study information on coverage protected areas (see Hopton of Australian ant communities. The two explanations and Mayer 2006) could also have been introduced into provided by these authors to account for this result can the SOM. This could help to find out if clusters with high also hold for our study. The first explanation is related species richness overlap with protected areas (Hopton to the locomotory behaviour of ants. The movements and Mayer 2006). This illustrates another important of ants are known to be more efficient and less asset of SOM: because it provides information on the constrained in simple than in complex environments relationship between species distribution and habitat (Kaspari and Weiser 1999). In simple environments, characteristics, SOM can be particularly helpful in ants can move quickly, easily recroît nestmates, and targeting the areas in which to focus conservation effort. defend/monopolize food sources efficiently against competitors and colonies can therefore develop more Acknowledgments W e thank Arnaud Legoff for assistance quickly. The second explanation is related to temper­ with field work and data collection. A.B. was financed by a doctoral grant from the Fundaci6 Crèdit Andorra. ature. Sites with a dense canopy cover are likely to be cooler than sites exposed to direct sunlight. 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