Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests.

Biol. Rev. (2014), pp. 000–000. doi: 10.1111/brv.12119

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Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests Laurent Augusto1,∗ , An De Schrijver2 , Lars Vesterdal3 , Aino Smolander4 , Cindy Prescott5 and Jacques Ranger6 1 UMR

1391 ISPA, INRA, Bordeaux Sciences Agro, Villenave d’Ornon, 33883 France & Nature Lab, Faculty of Bioscience Engineering, Ghent University, Geraardsbergse Steenweg 267 9090, Gontrode (Melle), Belgium 3 Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark 4 Vantaa Research Department, Finnish Forest Research Institute, PO Box 18, FI-01301 Vantaa, Finland 5 Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia Canada 6 Biogéochimie des écosystèmes forestiers, INRA, Centre de Nancy, 54280 Champenoux, France 2 Forest

ABSTRACT It has been recognized for a long time that the overstorey composition of a forest partly determines its biological and physical–chemical functioning. Here, we review evidence of the influence of evergreen gymnosperm (EG) tree species and deciduous angiosperm (DA) tree species on the water balance, physical–chemical soil properties and biogeochemical cycling of carbon and nutrients. We used scientific publications based on experimental designs where all species grew on the same parent material and initial soil, and were similar in stage of stand development, former land use and current management. We present the current state of the art, define knowledge gaps, and briefly discuss how selection of tree species can be used to mitigate pollution or enhance accumulation of stable organic carbon in the soil. The presence of EGs generally induces a lower rate of precipitation input into the soil than DAs, resulting in drier soil conditions and lower water discharge. Soil temperature is generally not different, or slightly lower, under an EG canopy compared to a DA canopy. Chemical properties, such as soil pH, can also be significantly modified by taxonomic groups of tree species. Biomass production is usually similar or lower in DA stands than in stands of EGs. Aboveground production of dead organic matter appears to be of the same order of magnitude between tree species groups growing on the same site. Some DAs induce more rapid decomposition of litter than EGs because of the chemical properties of their tissues, higher soil moisture and favourable conditions for earthworms. Forest floors consequently tend to be thicker in EG forests compared to DA forests. Many factors, such as litter lignin content, influence litter decomposition and it is difficult to identify specific litter-quality parameters that distinguish litter decomposition rates of EGs from DAs. Although it has been suggested that DAs can result in higher accumulation of soil carbon stocks, evidence from field studies does not show any obvious trend. Further research is required to clarify if accumulation of carbon in soils (i.e. forest floor + mineral soil) is different between the two types of trees. Production of belowground dead organic matter appears to be of similar magnitude in DA and EG forests, and root decomposition rate lower under EGs than DAs. However there are some discrepancies and still are insufficient data about belowground pools and processes that require further research. Relatively larger amounts of nutrients enter the soil–plant biogeochemical cycle under the influence of EGs than DAs, but recycling of nutrients appears to be slightly enhanced by DAs. Understanding the mechanisms underlying forest ecosystem functioning is essential to predicting the consequences of the expected tree species migration under global change. This knowledge can also be used as a mitigation tool regarding carbon sequestration or management of surface waters because the type of tree species affects forest growth, carbon, water and nutrient cycling.

* Address for correspondence (Tel: +33 (0)557122523; Fax: +33 (0)557122515; E-mail: [email protected]). Biological Reviews (2014) 000–000 © 2014 Institut National de la Recherche Agronomique. Biological Reviews © 2014 Cambridge Philosophical Society

L. Augusto and others

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Key words: spermatophytes, tree species, deciduous, evergreen, biogeochemical cycling, soil organic carbon, nitrogen. CONTENTS I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Effects of tree species groups on soil physical–chemical properties and water budget . . . . . . . . . . . . . . . (1) Physical and chemical properties of soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Water regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Do tree species groups modify the ecosystem carbon cycle? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Production of biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Production of dead organic matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (3) Decomposition of dead organic matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (4) Accumulation of organic carbon in soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Nutrient cycling as influenced by tree species groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Biogeochemical cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Forest demand for and use of nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Spermatophytes and forest functioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Understorey: an example of cascade effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (3) Knowledge gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (4) Tree species as mitigation tools? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Supporting information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I. INTRODUCTION The spatial current distribution of tree species is probably the result of the combined effects of plant diversification, climate, geography of soils [e.g. Brodribb, Pittermann & Coomes (2012)], and forestry activities. In the future, spatial distributions of tree species are expected to shift as a result of global change (Boucher-Lalonde, Morin & Currie, 2012), continuing changes from the past (Kutzbach, 1988). These shifts in spatial distribution will be towards higher latitudes (Cramer et al., 2001) and higher altitudes (Lenoir et al., 2008). Modelling and retrospective approaches predict large changes in the vegetation currently found in temperate and boreal biomes (Overpeck, Bartlein & Webb, 1991; Cramer et al., 2001). Evergreen gymnosperm (EG) trees in these regions are expected to extend the tree line to higher latitudes and altitudes but may be partly replaced by deciduous angiosperm (DA) trees in their current core areas as a result of natural migration and forest management decisions (Cramer et al., 2001). If DA forests differ in functioning compared to EGs, such changes of vegetation could have a large impact on global biogeochemical cycles given the large areas involved. The overstorey composition of a forest partly determines its biological and physical–chemical functioning (Ovington, 1953; Bocock & Gilbert, 1957; Binkley, 1995; Westoby & Wright, 2006). In some cases, the vegetation composition may even drive soil genesis

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(Graham & Wood, 1991; Willis et al., 1997). Therefore, changes in vegetation in the past may have shaped current soils, and future vegetation changes will likely modify ecosystem functioning. We investigated the extent to which changes in tree species, and in particular a shift from one functional or systematic group of tree species to another, could influence ecosystem functioning. We chose to simplify the question by focussing on the comparison between evergreen [sensu Chabot & Hicks (1982)] gymnosperm tree species on the one hand and deciduous angiosperm tree species on the other because they are the most widespread species groups in temperate and boreal climates. Other functional groups, like deciduous gymnosperms [e.g. Larix genus in boreal or alpine climates; see, however, Givnish (2002)] or evergreen angiosperms (e.g. certain Quercus species in Mediterranean climates), are not considered here. Among the many functions and services of forest ecosystems (Gamfeldt et al., 2013), we focus on those that could modify biogeochemical cycles, especially tree biomass production, soil carbon storage (sensu Gamfeldt et al., 2013), water budget and nutrient cycling (see online Appendix S1, for a brief introduction to forest functioning). To this end, we collected relevant literature, and carried out a meta-analysis and a narrative review (see online Appendix S2 for full explanations of the method used; references used for the meta-analysis are identified by an asterisk).

Biological Reviews (2014) 000–000 © 2014 Institut National de la Recherche Agronomique. Biological Reviews © 2014 Cambridge Philosophical Society

Spermatophytes and forest functioning Not all references comparing EG and DA species were included. Indeed, since the presence of a tree species is also the consequence of the local environment (Schurman & Baltzer, 2012), comparing tree species in very different contexts may bias conclusions (Binkley & Giardina, 1998). Similarly, the influence of vegetation composition on ecosystem functioning includes strong interactions with other factors like geology, initial soil properties, former land use, current management, succession stage of the ecosystem or climate (Augusto et al., 2002). This bias was clearly demonstrated by Stephenson & van Mantgem (2005) who showed that differences observed at the global, or at the biome, scale were no longer valid when the dataset was restricted to sites where both angiosperms and gymnosperms were growing in the same conditions. Interactions between tree species and site factors have in many cases hampered general conclusions on the specific functioning of tree species and may also be the source of conflicting evidence regarding the influence of tree species (Binkley, 1995). We consequently used scientific publications based on experimental designs where all species grew on the same parent material, had experienced similar initial soil conditions, and where the stage of development, former land use, and current management were similar. In most cases we selected EG and DA stands in common-garden designs, growing side by side, or close enough (i.e. horizontal distance and altitude) to ensure reliable comparisons in terms of environment (e.g. atmospheric deposition), climate, and soil. For a few specific functions, no study met these criteria and in such cases, we used all available references. We collected data on around 200 tree species from more than 100 genera in the lineage Pinophyta (i.e. coniferous gymnosperms but not Gingko, cycads, and gnetophyte gymnosperms) and eudicot angiosperms. In the angiosperm group, the genera occurring most frequently in the literature were from the families Betulaceae (genus Betula) and Fagaceae (Fagus and Quercus; see online Table S1, Appendix S1 for more details). Species from other angiosperm families were well represented in our review, such as the genera Acer , Nothofagus or Tilia (see online Table S1, Appendix S1). There are three main conifer families in the gymnosperm group, in terms of number of species (Brodribb et al., 2012): Pinaceae, Cupressaceae, and Podocarpaceae. The family Pinaceae was the most abundant in our database (genera Abies, Pseudotsuga, Tsuga, Picea and Pinus). However, a non-negligible number of studies also focussed on species from the families Cupressaceae (mainly Chamaecyparis and Thuja, but also Cryptomeria, Cunninghamia, Juniperus, Sequoia, etc.) and Podocarpaceae (mainly Podocarpus). Plants may influence ecosystem functioning through their functional traits (Binkley & Giardina, 1998). Because EG species differ from DA species for many traits such as leaf structure, photosynthetic capacity,

3 hydraulic network, and tissue composition (see Sections II and III), we hypothesized that DAs differ from EGs with respect to forest ecosystem functioning and that a climate-change-induced shift from one group to the other will directly or indirectly affect forest growth, and carbon, water and nutrient cycling. This review provides an overview of the mechanisms and processes involved, and assesses the extent to which changes in tree species will contribute to effects of global change on forest ecosystem functioning.

II. EFFECTS OF TREE SPECIES GROUPS ON SOIL PHYSICAL–CHEMICAL PROPERTIES AND WATER BUDGET (1) Physical and chemical properties of soil Physical properties of soils (such as bulk density, strength, porosity, water infiltration and retention capacity, erosion susceptibility) depend to a great extent on the soil’s textural and mineralogical composition, organic C (Corg ) content, activity of soil fauna, and pH. Soil texture and mineralogical composition are not likely to be significantly modified by tree species, except in the very long term. In the same way, soil Corg content appears to differ little between tree species types (see Sections II and III). This is consistent with soil physical properties (measured per gram of soil) often being described as not, or only slightly, dependent on tree species (Nihlgård, 1971; Grieve, 1978; Cannell, 1999; Augusto et al., 2002). However, counter-examples exist (Scott, 1998) and may reflect that some soil physical properties (e.g. structure, porosity) also depend on soil pH and activities of soil fauna. Indeed, the latter variables can strongly discriminate EG from DA forests (see Section III.3 for soil fauna). EG trees tend to acidify the topsoil [i.e. decrease the pH, generally by a few tenths of a pH unit, and/or the saturation in non-acidic exchangeable cations (K+ , Ca2+ , Mg2+ )] and the soil solution compared to DAs (Ovington, 1953; Nihlgård, 1971; Binkley & Valentine, 1991; Ranger & Nys, 1994; Binkley, 1995; Finzi, Canham & Van Breemen, 1998a; Strobel, Bernhoft & Borggaard, 1999; Augusto & Ranger, 2001; Strobel et al., 2001; Augusto et al., 2001b; Augusto, Dupouey & Ranger, 2003; Dijkstra & Fitzhugh, 2003; Hagen-Thorn et al., 2004b; Légaré, Paré & Bergeron, 2005; Reich et al., 2005; Oostra, Majdi & Olsson, 2006; Gartzia-Bengoetxea, Gonzalez-Arias & de Arano, 2009; Hansson et al., 2011; Lindroos et al., 2011; Eisalou et al., 2013) with a few exceptions (Ayres et al., 2009; Mareschal et al., 2010). This common result does not imply that soils are neutral under DAs and acid under EGs, which would be a systematic and simplistic statement. Indeed, soils can be very acid under DAs (e.g. Ovington, 1953). In the same way, neutral or basic soils


4 could be found under EGs (e.g. Ovington, 1953). The acidifying trend observed in EG forests in comparison to DA forests is a relative difference. This difference in soil acidity can be caused by various processes including different deposition of potentially acidifying atmospheric pollutants [see Section IV and de Schrijver et al. (2012)], more acidic and nutrient-poor litterfall in EGs (e.g. Nykvist, 1963; Chabot & Hicks, 1982; Priha & Smolander, 1997; Hagen-Thorn et al., 2004a; Yuan & Chen, 2009a; Hansson et al., 2011; de Schrijver et al., 2012; but see Hansen et al., 2009), dissimilar rates of nutrient uptake (de Schrijver et al., 2012), differences in the production of organic acids during decomposition of litter (Binkley & Valentine, 1991; Binkley, 1995; Raulund-Rasmussen et al., 1998; Dijkstra et al., 2001; Rothe, Kreutzer & Kuchenhoff, 2002b; Dijkstra & Fitzhugh, 2003; Mueller et al., 2012a), and dissimilar production of acid exudates in the rhizosphere (Calvaruso, N’Dira & Turpault, 2011). (2) Water regime The leaf area index [LAI in m2 of projected leaf surface per m2 of soil surface; see Bolstad & Gower (1990) and Iio et al. (2013) for more details on methods] of mature EG forests can be higher than in DA forests [Iio et al. (2013), Niinemets (2010); a in Fig. 1, see online Table S2, Appendix S2]. Due to their higher LAI and to their persistent foliage, EGs intercept more precipitation than DAs (common range of interception rate: EG = 25–40%; DA = 15–25%), even during the growing season (Nihlgård, 1970; Aussenac, 2000; Augusto et al., 2002; Armbruster, Seegert & Feger, 2004; Breda et al., 2006; Barbier, Balandier & Gosselin, 2009; Hojjati, Hagen-Thorn & Lamersdorf, 2009; Christiansen et al., 2010; Carnol & Bazgir, 2013). The intercepted water is retained by capillarity on foliage, and to a lesser extent on branches and stems, and is eventually evaporated before it reaches the soil surface. This interception process causes soils to be drier under EG species than under other tree types (Nihlgård, 1969, 1971; Ranger & Nys, 1994; Augusto & Ranger, 2001; Lee et al., 2010; Vesterdal et al., 2012). Because of the high interception rate of precipitation by EGs, water discharge towards surface water and groundwater is also generally low (typically ∼30% lower) under these tree species (Ranger & Nys, 1994; Swank & Vose, 1994; Armbruster et al., 2004; Komatsu, Kume & Otsuki, 2008; Christiansen et al., 2010). EGs are reported to have similar or slightly lower water-use efficiencies than DAs (Kuglitsch et al., 2008). Assuming similar carbon assimilation, this implies a higher transpiration flux. However, such a systematic difference has not been reported thus far (Augusto et al., 2002). To sum up, forest water regime varies among tree species (Aranda et al., 2012). The presence of EGs results in a low rate of precipitation input into the


Fig. 1. Mean differences in functioning between evergreen gymnosperm (EG) species and deciduous angiosperm (DA) species. (a) LAI = leaf area index (m2 foliage m−2 ). (b) Forest growth (biomass annual increment). (c) Litterfall flux (Mg ha−1 year−1 ). (d) Forest floor mass (Mg ha−1 ). (e) N mineralization flux (μg-N g-soil −1 day−1 ). (f ) Nitrification flux (μg-N g-soil −1 day−1 ). Values were taken from studies published in peer-reviewed journals (112 cases studies collected from references identified by asterisks in the reference list). To avoid pseudo-replication, only one mean value was calculated per study. Values are presented as relative values = (EG value/DA value); positive and negative relative values represent EG values higher or lower than DA values, respectively. For the sake of clarity, comparisons between EG and DA are also presented as the mean arithmetic difference (% higher or lower). Significant differences from zero (i.e. significant difference between EG and DA at P ≤ 0.05; ‘n.s.’, P > 0.05) were tested with a t-test and a bootstrap confidence interval (using log relative values). For N fluxes, values of forest floor and/or topsoil were used (no effect of the soil layer was observed in the distribution of relative values). See Appendix S2 for a full description of the methods used, and Table S2 in Appendix S2 for absolute mean values.

forest ecosystem, drier soil conditions and lower water discharge.

III. DO TREE SPECIES GROUPS MODIFY THE ECOSYSTEM CARBON CYCLE? At the ecosystem scale, the carbon cycle depends on the processes involved in carbon fixation and stabilization in the ecosystem, and outputs via respiration and Corg decomposition. Here we investigate if the type of tree species influences the major processes involved in the ecosystem carbon cycle, namely (i) biomass production (involved in C sequestration), (ii) production of dead organic matter (i.e. dead Corg ), (iii) Corg decomposition rate (i.e. C mineralization) and (iv) C stabilization in soil.


Spermatophytes and forest functioning (1) Production of biomass When fully supplied with light, water and nutrients, angiosperm seedlings have, on average, a higher relative growth rate [RGR in grams of biomass produced per day and per gram of initial biomass; see Hoffmann & Poorter (2002) for details of the methodological biases of this approach] than gymnosperm seedlings (e.g. Cornelissen, Diez & Hunt, 1996). The higher RGR of DAs may be a consequence of higher foliage nitrogen (N) content, photosynthetic capacity (in μmol-CO2 m−2 -leaf s−1 ), water-use efficiency, specific leaf area (SLA; mm2 mg−1 ), specific root length (SRL; m g−1 ) and the ratio between foliage area and plant biomass (Chabot & Hicks, 1982; Aerts, 1995; Reich et al., 1995, 1998; Cornelissen et al., 1996, 1997; Reich, Walters & Ellsworth, 1997b; Wright & Westoby, 1999; Castro-Diez, Puyravaud & Cornelissen, 2000; Comas & Eissenstat, 2004; Warren & Adams, 2005; Kuglitsch et al., 2008; Brodribb & Feild, 2010; Alvarez-Uria & Korner, 2011; Hansson et al., 2013b). Conversely, global and regional surveys indicate that EG stands are often as productive as DA stands (Jordan & Murphy, 1978; Aerts, 1995; Reich et al., 1997a; Reich & Bolstad, 2001; Huston & Wolverton, 2009). In some cases, EG stands are more productive (Ovington, 1956; Downing & Weber, 1984; Pretzsch, 2009; Hynynen, Repola & Mielikäinen, 2011). Direct comparisons of the productivity of different forests are probably biased because species are not randomly distributed and may thus be located on soils of different fertility (Binkley, 1995; Wilson & Campbell, 1996; Stephenson & van Mantgem, 2005; Huston & Wolverton, 2009). We controlled for this by compiling data on the biomass produced by EG and DA stands growing under similar conditions, ideally in adjacent plots. Stands that were not at the same stage of development were excluded because forest productivity is strongly correlated to stand biomass or age (Downing & Weber, 1984; Huston & Wolverton, 2009). Our results suggested that EG forest stands produce on average more aboveground biomass than DA stands (b in Fig. 1), at least for tree species selected by foresters for production purposes. Inclusion of non-commercial EG species may reduce the difference, like that observed in semi-natural forests where both groups grow at the same rate (Reich et al., 1997a; Reich & Bolstad, 2001), or might even reverse the trend (Enright & Hill, 1995) because slow-growing species and fast-growing species are encountered in both groups (Comas & Eissenstat, 2004). Several aspects of tree ecology may account for the apparent discrepancy between forest surveys and pot experiments based on seedlings. First, unlike seedlings, adult EG trees have several annual cohorts of foliage whereas DA trees, by definition, have only one (Chabot & Hicks, 1982; Aerts, 1995). This trait means that the forest LAI can be higher for mature EG forests [a in Fig. 1; Niinemets (2010)] and thus compensate for the

5 lower photosynthetic capacity of EG species (Chabot & Hicks, 1982; Bond, 1989; Aerts, 1995). Secondly, experiments on seedling RGR were carried out under optimal growth conditions, implying that the measured gains in biomass were specific maximum values (i.e. RGRmax ). Under natural conditions, tree growth may be limited by site factors such as temperature and water availability (Nemani et al., 2003) or the supply of bioavailable nutrients (Elser et al., 2007). Under these conditions, the effective RGR of a species depends on its RGRmax and on its plasticity (i.e. its ability to maintain a growth rate close to maximum even with low levels of resources). In their evolutionary theory, Chapin, Autumn & Pugnaire (1993) proposed that low-resource species (like most EG species) have lower RGRmax and photosynthetic rates than high-resource species (like most DA species), but are better able to maintain growth in infertile sites. In this scheme, low-resource species may outcompete high-resource species in unfavourable environments [due to higher survival rate, growth rate, or nutrient use efficiency; e.g. Fassnacht & Gower (1999)] but are outpaced in sites more favourable to growth (Coley, Bryant & Chapin, 1985; Chapin et al., 1993; Grime et al., 1997). The experiment reported by Karlsson & Nordell (1987) is an example of such a pattern. In a treatment with low nutrient supply, growth of Betula pubescens was ∼40% lower than that of Pinus sylvestris, a stress-tolerant species (i.e. with low nutrient requirements), whereas growth of the angiosperm species was ∼70% higher in a treatment with high nutrient supply. Temperate and boreal forests are commonly limited by the supply of nutrients [mainly nitrogen, phosphorus and, probably to a lesser extent, potassium or calcium (Vitousek & Howarth, 1991; Tripler et al., 2006; Elser et al., 2007; Harpole et al., 2011)] or by the local climate (Nemani et al., 2003). Moreover, in temperate and boreal regions, the trait of being evergreen means being able to extend the length of the growing period because photosynthesis may start before the last spring frost and continue after the first autumn frost (Givnish, 2002; van Ommen Kloeke et al., 2012). This relative advantage increases with a decrease in the length of the growing season (Givnish, 2002) because of larger relative importance of the edges of the growing season (Chabot & Hicks, 1982). Based on these studies, we conclude that biomass production may be lower in DA stands than in EG stands. The natural conditions in parts of the mid and high latitudes are unfavourable enough to prevent many DA tree species from taking advantage of their high growing potential, particularly on poor soils. (2) Production of dead organic matter Litterfall fluxes are controlled at the global scale by climate (Jordan & Murphy, 1978; Vogt, Grier & Vogt, 1986; Liu et al., 2004) and to a lesser extent by the fertility of the local site (Bolstad, Vose & McNulty, 2001; Hansen et al., 2009). Globally, litterfall flux (i.e. foliage


6 and small woody fragments; Mg ha−1 year−1 ) decreases with increasing latitude and decreasing site fertility. When growing on the same site, i.e. same climate and similar soil fertility, forests of DAs produce on average similar amounts of litterfall as EG forests [see for instance Trum et al. (2011), Vesterdal et al. (2008) and c in Fig. 1]. In contrast to aboveground litterfall, fine root turnover in forests is poorly predicted by climate (Gill & Jackson, 2000) and there is little correlation with local aboveground litter production (Hobbie et al., 2010). There is currently no clear pattern of how, and indeed if, the type of tree species modifies the production of belowground dead organic matter. Attempts to compare fine root biomass, production and turnover of angiosperm tree species with gymnosperm tree species has produced inconsistent rankings (Oostra et al., 2006; Withington et al., 2006; Yuan & Chen, 2010; Finer et al., 2011a,b; Hansson et al., 2013b). This inconsistent picture reflects the paucity of adequate data and the time-consuming nature of root studies (Brodribb et al., 2012). In the same way, no clear difference of rooting depth seems to exist between temperate deciduous and coniferous forests (Jackson et al., 1996). As a whole, further investigations are required on belowground processes. We conclude that aboveground production of dead organic matter appears to be of the same order of magnitude among tree species groups growing in the same site, but further research is required to quantify differences in belowground litter input and differences in maximum depth of rooting. (3) Decomposition of dead organic matter The decomposition of organic matter is a biological process controlled by (i) moisture, (ii) temperature, (iii) abundance and composition of the soil biota community, and (iv) the composition of organic matter. At the global scale, decomposition of aboveground litter is strongly related to climate (i.e. temperature and humidity), whereas at the biome, regional, or local scale (i.e. at scales with narrower ranges of climatic conditions), decomposition is mainly correlated with tissue chemistry and its interaction with site properties (Harmon et al., 1986; Aerts, 1997; Gholz et al., 2000; Hoorens, Coomes & Aerts, 2010; Prescott, 2010; Duboc et al., 2012; Makkonen et al., 2012; Waring, 2012). In mesic conditions, the lower soil moisture content under EG species than under DA species may reduce the decomposition rate of dead organic matter through a depressive effect on soil biota. Within a given climate, soil temperature is tightly linked to the proportion of sunlight that penetrates the tree canopy and reaches the forest floor, and so to the light interception rate of trees. Angiosperms and gymnosperms are not systematically distinguished according to light interception rates (Augusto et al., 2003; Cavard et al., 2011;

L. Augusto and others Yilmaz, Sevgi & Koc, 2012) as it corresponds to different levels of shade tolerance and shading strategies rather than to a functional trait typical of these two groups. The amount of light reaching the forest floor depends on the proportion of shade-intolerant species in the overstorey (Messier, Parent & Bergeron, 1998) and both angiosperms and gymnosperms contain shade-intolerant (e.g. Betula populifolia and Pinus sylvestris) and shade-tolerant species [e.g. Fagus sylvatica and Tsuga canadensis; Hewitt (1998)]. It should be noticed, however, that some exceptions exist, particularly in unthinned young plantations of some genera, like Picea or Abies, where shading can be extremely high (Ovington, 1955; Pigott, 1990; Augusto et al., 2001a). This lack of clear and generalized difference in light interception explains the ecosystem temperature pattern. Indeed, air and soil temperatures are not strongly different under neighbouring DA and EG stands. In most cases, soil temperature is only ∼0–1∘ C higher under a DA canopy than under an EG canopy (Augusto et al., 2002; Wang, Yang & Zhang, 2006; Ayres et al., 2009; Lee et al., 2010; Laganière et al., 2012; Akburak & Makineci, 2013), except at the beginning of spring when DA foliage is still not fully expanded and sunlight influx is increasing (Lee et al., 2010). Because of this small difference between DA and EG forests, soil temperature is not likely to systematically distinguish decomposition rates of DAs and EGs, even if differences among tree species exist (Laganière et al., 2012). In general, tree species groups do not modify much the amount of soil microbial biomass (Mardulyn et al., 1993; Pelissier & Souto, 1999; Kanerva & Smolander, 2007; Ayres et al., 2009; Kubartova et al., 2009), although in some studies microbial biomass was higher under DAs than under EGs (Priha & Smolander, 1999; Priha et al., 2001; Smolander & Kitunen, 2011). Conversely to microbial biomass, the composition of the soil microbial community (e.g. the ratio of bacteria to fungi) is partly (Snajdr et al., 2013), but significantly, influenced by litter chemistry (Priha et al., 2001; Leckie, Prescott & Grayston, 2004; Grayston & Prescott, 2005; Kubartova et al., 2009; Thoms et al., 2010; Prescott & Grayston, 2013). This partial relationship reflects the influence of litter properties (such as C:N ratio, N:P ratio, Ca content, pH or, in particular conditions, allelopathic compounds) which modify litter degradability and the environmental conditions for soil microbes (Muller, 1969; Norby & Kozlowski, 1980; Pelissier & Souto, 1999; Hogberg, Hogberg & Myrold, 2007; Gusewell & Gessner, 2009; Thoms et al., 2010; Prescott & Grayston, 2013). The presence of soil fauna strongly enhances litter disappearance and decomposition (Hättenschwiler & Gasser, 2005; Jacob et al., 2009). Among animal species found in the soil, earthworms have an extremely significant effect on litter decomposition (Reich et al., 2005; Hobbie et al., 2006) whereas other animals, like microarthropods, have much less


Spermatophytes and forest functioning (although still significant) influence (Kampichler & Bruckner, 2009). Earthworm abundance is generally low under EGs (Chapman, Whitaker & Heal, 1988; Saetre, 1998; Reich et al., 2005; de Schrijver et al., 2012; Frouz et al., 2013; Hansson et al., 2013a), which is an additional limitation to litter decomposition. With regard to litter chemistry, high nutrient content (N, P, K, Ca, Mg, Mn), pH, and cellulose content are positively correlated with rapid litter decomposition whereas high lignin or hemicellulose content, foliage toughness, C:N, C:P, lignin:N and lignin:P ratios slow down the process (Berg et al., 1996; Inagaki, Miura & Kohzu, 2004; Hättenschwiler & Gasser, 2005; Reich et al., 2005; Hobbie et al., 2006; Cornwell et al., 2008; Rock, Badeck & Harmon, 2008; De Santo et al., 2009; Jacob et al., 2009, 2010; Hoorens et al., 2010; Laganière, Paré & Bradley, 2010; Prescott, 2010; Makkonen et al., 2012; Talbot & Treseder, 2012; Vesterdal et al., 2012; Carnol & Bazgir, 2013; Jackson, Peltzer & Wardle, 2013). The chemistry of litter, and of aboveground tissues in general, markedly differentiates DAs from EGs. The former have higher nutrient contents (N, P, K, Ca, Mg, etc.; generally between +30 and +90%; see references below), pH, starch content and a higher N:P ratio than the latter. Conversely, EG species are often characterized by foliage that is sclerophyllous, rich in C, lignin and Mn, and with high C:N and C:P ratios (Chabot & Hicks, 1982; Harmon et al., 1986, 2013; Aerts, 1995; Brandtberg, Lundkvist & Bengtsson, 2000; Augusto et al., 2003; Inagaki et al., 2004; Hagen-Thorn et al., 2004a; Gartzia-Bengoetxea et al., 2009; Yuan & Chen, 2009b; Li et al., 2010; Sardans, Rivas-Ubach & Penuelas, 2011; de Schrijver et al., 2012; Skorupski et al., 2012; Vergutz et al., 2012; Achat et al., 2013; Jackson et al., 2013; Cools et al., 2014; Cornwell et al., 2014). Among basic compounds, cellulose structures are similar irrespective of the tree species but the structure of hemicellulose varies between these two plant groups; xylose is the major hemicellulose sugar in angiosperms whereas gymnosperms contain both xylans and glucomannans. Gymnosperm lignins are typically composed of guaiacyl units whereas angiosperm lignins are largely composed of similar levels of guaiacyl and syringyl units (Gomez-Ros et al., 2007). Moreover, there are major differences between gymnosperms and angiosperms in secondary metabolites, of which terpenes and phenolic compounds are probably the most abundant. These differences may affect the degradation of litter, and subsequently soil processes and properties. Based on these characteristics of the litter, it would be expected that rates of decomposition would be lower in EG than in DA forests, and this inference is confirmed by many studies of foliar decomposition rates [generally −25 to −55% (Cornwell et al., 2008; Kubartova et al., 2009; Laganière et al., 2010; Duboc et al., 2012; Xu et al., 2012; Hansson et al., 2013a; Osono, Azuma & Hirose, 2014)]. But there are exceptions to this general trend (Prescott et al., 2000) showing that

7 the relative role of the different factors that influence litter decomposition, and the way they interact, is a complex question (Makkonen et al., 2012). First, some factors, like the presence/absence of soil fauna (Hättenschwiler & Gasser, 2005) or site properties (Vesterdal, 1999; Prescott, 2002; De Santo et al., 2009), may change the decomposition process in a way that patterns are reversed. Secondly, it is now recognized that foliar litter decomposition is not a homogeneous process over time as the processes controlling the early stage of decomposition differ from those controlling the later stages (Berg, 2000; Kalbitz et al., 2006; Klotzbucher et al., 2011; Hobbie et al., 2012). This may lead to litters, such as many DA litters, that decompose quickly initially, having slower late-stage decay rates (or lower maximum decomposition limits) than litters such as needle litter from EGs (Berg et al., 1996; Cotrufo et al., 2013). According to some authors, the early-stage decomposition of litter is controlled by foliage lignin and cellulose contents, N and P contents, and associated ratios [lignin:N, C:N, lignin:P, C:P (Aerts, 1997; Berg, 2000; Sariyildiz & Anderson, 2003; Inagaki et al., 2004; Hättenschwiler & Gasser, 2005; Cornwell et al., 2008; Rock et al., 2008; Vesterdal et al., 2008; Hoorens et al., 2010; Laganière et al., 2010; Prescott, 2010; Talbot & Treseder, 2012)]. In this view, nutrients and cellulose (i.e. an energy source) enhance the decomposing activity of microbes whereas lignin is seen as a difficult compound to degrade (Voriskova et al., 2011; Talbot & Treseder, 2012). Conversely, according to other authors, the role of lignin content is insignificant at the early stage (Kalbitz et al., 2006). Indeed, some other foliar litter properties, like Ca content, content of phenolic compounds, or pH, may be correlated much better with litter decomposition (Reich et al., 2005; Hobbie et al., 2006; Holdsworth, Frelich & Reich, 2012) because such properties control soil microbial composition (Prescott & Grayston, 2013) and the decomposing activity of earthworms and soil fauna in general (Horner, Gosz & Cates, 1988; Kraus, Dahlgren & Zasoski, 2003; Binkley & Menyailo, 2005; Reich et al., 2005; Kasurinen et al., 2007; Meehan, Crossley & Lindroth, 2010). We suggest that these two different theoretical frameworks may be the result of different experimental designs in interaction with soil fauna. Indeed, earthworm abundance is mainly positively correlated with soil pH and exchangeable Ca2+ [i.e. negatively correlated to soil acidity: see Blaser, Pannatier & Walthert (2008) and Chadwick & Chorover (2001) for more details on the partial relationship between soil pH and exchangeable cations, like Ca2+ ]. Earthworm abundance is consistently nil in very acidic soils and high in neutral soils (Springett & Syers, 1984; Fragoso & Lavelle, 1992; Muys, Lust & Granval, 1992; Neirynck et al., 2000; Joschko et al., 2006). We speculate that studies which used a narrow range of soil acidity (Talbot & Treseder, 2012), or were carried out on neutral soils (Hättenschwiler & Gasser, 2005) or which compared


8 different sites (Inagaki et al., 2004) were not able to provide evidence for the role of litter Ca and pH because the conditions required to influence clearly the activity of earthworms were not met. Conversely the study of Reich et al. (2005), carried out in a single site but with a wide range of soil pH (3.9–5.0) and soil base saturation (28–53%), was able to show direct relationships among litter Ca content, earthworm activity and litter decomposition. In other cases, earthworm activity may have been less variable and other factors drove litter decomposition. However, whatever the main factor locally driving litter decomposition (i.e. litter lignin:N ratio versus litter Ca content), DA litter is generally, but not systematically, decomposed quicker than EG litter. The influence of tree species groups on root decomposition to a certain extent resembles, but also differs from that on the litter decomposition rate (Hobbie et al., 2010). Like litter decomposition, root decomposition is driven by climate and root chemistry (Silver & Miya, 2001). But, unlike aboveground litter, tree root decomposition appears to be controlled primarily by root properties and secondarily by climate (Silver & Miya, 2001). The less important role of climate in root decay may be due to the buffering effect of soils to variations in temperature and moisture. In a given site, both root composition (i.e. N, P, Ca and lignin contents) and root morphology (diameter, SRL) of DAs indicate that they are more favourable to decomposition than are roots of EGs (Silver & Miya, 2001; Withington et al., 2006; Ostonen et al., 2007; Li et al., 2010; Yuan & Chen, 2010; Lei, Scherer-Lorenzen & Bauhus, 2012; Hellsten et al., 2013; Sun, Mao & Han, 2013). However, no consensus has been reached on ranking of root properties which most influence decomposition rates. According to Silver & Miya (2001) and Prescott (2010), roots decompose rapidly when they are characterized by high Ca content and low values of lignin:N ratio, like aboveground litter. Conversely, Hobbie et al. (2010) concluded from their field study that factors in control of root decomposition do not mirror those for litter decomposition, as the former is controlled by hemicellulose content and root diameter. (4) Accumulation of organic carbon in soils Aboveground litterfall inputs were largely similar within similar climate and soil type (c in Fig. 1), so the lower decomposition rates under EGs results in higher stocks of dead organic matter in the forest floor [d in Fig. 1; see for instance Borken & Beese (2005b), Finzi, Van Breemen & Canham (1998b), France, Binkley & Valentine (1989), Graham & Wood (1991), Ovington (1954), Trum et al. (2011), Vesterdal et al. (2013) and Vesterdal & Raulund-Rasmussen (1998)]. Input of organic matter to soils is enhanced by the activity of anecic earthworms that incorporate litter into mineral soil layers through their intense burrowing activity (Binkley, 1995; Quideau et al., 1998; Prescott, 2010; Vesterdal et al., 2013). DA

L. Augusto and others forests may thus enhance litter incorporation due to more favourable conditions, in terms of pH and soil Ca, to earthworms and other decomposers in general (Frouz et al., 2013). However, once incorporated or even humified, organic matter could be mineralized or stabilized. It was long believed that stabilized soil organic matter mainly comprised remains of recalcitrant aboveground and belowground litters. Recent studies have provided evidence that stabilized organic matter is mainly the result of microbial processing, with physical–chemical interactions with soil particles (Miltner et al., 2012; Cotrufo et al., 2013). The transformation of decomposing organic matter into stabilized forms thus depends on (i) inhibition of microbes through recalcitrance of organic matter to degradation, (ii) flocculation–aggregation processes, and (iii) cation bridging with Ca, Al-Fe oxides or clay minerals (von Lutzow et al., 2006; Hobbie et al., 2007; Jandl et al., 2007; Cotrufo et al., 2013). Most of these processes are not primarily tree-species-dependent, except recalcitrance to degradation (Hobbie et al., 2007). Conversely, these processes could be indirectly enhanced or mitigated by selecting tree species which influence these processes by modifying other soil properties like nutrient content or pH (see Section II.1). Some species-specific differences have been revealed for the recalcitrance of dissolved Corg (DOC) to degradation (Don & Kalbitz, 2005; Kiikkilä, Kitunen & Smolander, 2006, 2011) and there were indeed differences in the chemical composition of dissolved organic matter between different tree species (Kalbitz et al., 2006; Kiikkilä et al., 2012; Uselman, Qualls & Lilienfein, 2012). However, we do not know whether these DOC properties differ clearly between EGs and DAs, both in terms of DOC concentrations (Michalzik et al., 2001) and degradability (see above references). Moreover, according to a review by von Lutzow et al. (2006), protection through chemical recalcitrance affects the rate of loss but not the residual amount of organic matter because, given enough time, soil biotic communities are able to disintegrate all organic matter. Based on these concepts, DA species may differ slightly from EG species, or display a similar pattern, in their ability to modify accumulation of stable carbon stocks in soils. Owing to their higher N (and Ca) content and the favourable conditions they create for earthworms, some DA species may enhance accumulation of stable carbon stocks in soils (Berg et al., 1996; Prescott, 2010; Cotrufo et al., 2013; Vesterdal et al., 2013). However, this general statement should be nuanced because of interactions with many processes. For instance, an opposite result may be observed in sites where EG stand productivity, and consequently production of dead Corg , is far higher than productivity of DA stands (Gurmesa et al., 2013). This topic requires further investigation to see if a consistent pattern develops with respect to


Spermatophytes and forest functioning taxonomic grouping of trees. Indeed, in mineral soil layers, rate of carbon mineralization (i.e. a process which influences soil Corg accumulation through its antagonist nature) appears to be more dependent on site properties than on tree properties (Raich & Tufekcioglu, 2000; Ladegaard-Pedersen, Elberling & Vesterdal, 2005; Hobbie et al., 2007; Lee et al., 2010; Smolander & Kitunen, 2011). Raich & Tufekcioglu (2000) compiled data on soil respiration (CO2 emission being the final step of Corg mineralization) and found that this flux was mainly controlled by site fertility (as estimated by stand growth) and only marginally by the type of forest, coniferous stands having 10% lower soil respiration than broadleaved stands [for single case studies, see e.g. Akburak & Makineci (2013), Borken & Beese (2005b), Laganière et al. (2012), Lee et al. (2010) and Wang et al. (2006)]. This small difference, which has not been found consistently (Subke, Inglima & Cotrufo, 2006; Vesterdal et al., 2012), may be the consequence of the higher soil C:N ratio (see Section IV.1) frequently reported in coniferous stands compared to broadleaved stands [e.g. Augusto et al. (2003) and Cools et al. (2014)]. Indeed, among the numerous variables (e.g. soil P or exchangeable Al and Fe) which could influence the mineralization of soil organic carbon (i.e. soil heterotrophic respiration) in multi-species comparisons, the soil C:N ratio has been shown to have a consistent negative influence (Borken et al., 2002; Ladegaard-Pedersen et al., 2005). In conclusion, the difference between DAs and EGs in soil carbon cycling does not seem to be large. The production of dead organic matter (foliage and fine roots) and its mineralization, or stabilization, are fairly similar or show, on average, limited differences. The role of belowground processes related to root dynamics remains however unclear and requires further study. Because of the chemical properties of their tissues, higher soil moisture and favourable conditions for earthworms, DAs may enable quicker decomposition at early stages but greater stabilization at late stages. It remains unclear if, at the scale of a few decades, accumulation of carbon in soils (i.e. forest floor + mineral soil) differs between the two types of tree species (Augusto et al., 2003; Vesterdal et al., 2008; Achat et al., 2013; Laganière et al., 2013) as differences in forest floor and mineral soil may offset each other (Vesterdal et al., 2013). It is practically difficult to produce evidence for differences in soil carbon stock because the stocks are very large compared to annual fluxes (a few dozen or hundreds of Mg-C ha−1 versus a few Mg-C ha−1 year−1 , respectively) and soil stocks are difficult to monitor precisely because of the high spatial variability of forest soils (Lindner & Karjalainen, 2007). Moreover, part of soil organic matter may be very old (i.e. several millennia) and was consequently not produced by the current forest vegetation [see for instance Kulakova (2012)], making interpretation of differences among tree species

9 tricky, particularly in studies without common-garden or paired-stand designs. Even if the influence of tree species on long-term accumulation of the stabilized Corg pool in soils remains unclear, it appears that the turnover rate of the soil pool of recent Corg (i.e. less than a few years) may be higher under DAs than under EGs (Hansen et al., 2009; Vesterdal et al., 2012). This possible difference in ecosystem functioning should be visible when considering the cycling of nutrients among the different compartments of the soil–plant system, as shown in Section IV.

IV. NUTRIENT CYCLING AS INFLUENCED BY TREE SPECIES GROUPS (1) Biogeochemical cycles Despite lower SLA (Poorter et al., 2009), the higher LAI in EG than in DA tree species (Iio et al., 2013), and probably some anatomic differences (Räsänen et al., 2013), result in a more efficient filtering of atmospheric deposition of elements (generally >+25% for Na, S, and N), even during the growing season and with moderate levels of pollution (Bergkvist & Folkeson, 1995; Robertson, Hornung & Kennedy, 2000; Augusto et al., 2002; Rothe et al., 2002a; de Schrijver et al., 2007, 2008; Berger et al., 2009a,b; Christiansen et al., 2010). When the ambient level of atmospheric deposition is moderate to high (due to anthropogenic pollution), there is a clear positive correlation between atmospheric deposition and fluxes of elements (e.g. N, S, Ca) to surface waters through water seepage (de Schrijver et al., 2007; van der Salm et al., 2007). In such a context, the high efficiency of EG tree species in capturing atmospheric deposition can result in high losses of elements in seepage water (Bergkvist, 1987; Ranger & Nys, 1994; Bergkvist & Folkeson, 1995; Augusto et al., 2002; Rothe et al., 2002a; de Schrijver et al., 2007) and in possible watershed acidification when soils and bedrocks have low acid buffer capacities (Alexander & Cresser, 1995). In contexts where ambient atmospheric pollution is low, there is no clear correlation between atmospheric deposition and losses of mineral elements to surface waters (Berger et al., 2009a) and tree species ranking may be less consistent (Berger et al., 2009b; Christiansen et al., 2010). Tree nutrition in forests under low atmospheric deposition depends more on organic matter recycling than on input–output fluxes. Dead organic matter may be decomposed and release mineral N (i.e. NH4 + or NO3 − through mineralization and nitrification), which are the main bioavailable forms of N for plants, with the exception of those plant species able to take up simple organic N compounds through their roots and mycorrhizal associates (Näsholm, Kielland & Ganeteg, 2009). N mineralization and nitrification rates are


10 highly dependent on the composition of the overstorey stratum: several studies have shown that net fluxes of N mineralization and nitrification, from forest floor and from the mineral soil, were larger under DAs than under EGs [e and f in Fig. 1; see, for instance, Côté et al. (2000), Smolander et al. (2005), Son & Lee (1997), Wang & Fernandez (1999) and Welke & Hope (2005)]. However, this pattern is far from consistent and several exceptions also have been reported (Nihlgård, 1971; Binkley & Valentine, 1991; Gower & Son, 1992; Finzi et al., 1998b; Lovett et al., 2004; Smolander & Kitunen, 2011). One common explanation proposed to explain low N mineralization and nitrification rates is the more acidic conditions imposed by EGs (see Ste-Marie & Paré, 1999), the high C:N ratio of EG dead Corg , and its high content in compounds which are inhibitory to soil microbes or form recalcitrant complexes with soil organic N [e.g. certain polyphenols and terpenes (Northup et al., 1995; Schimel & Bennett, 2004; Smolander et al., 2012)]. Another explanation could be the nature of the mycorrhizae associated with tree species. Indeed, whereas most EG species of the family Pinaceae are associated with ectomycorrhizal (ECM) fungi, DA species can be associated with ECM and/or arbuscular mycorrhizal (AM) fungi (Cornelissen et al., 2001). Several studies showed that N mineralization was significantly higher under tree species associated with AM fungi than under those associated with ECM fungi (Cornelissen et al., 2001; Phillips & Fahey, 2006). Under this hypothesis, DA species associated with AM fungi (e.g. species of the genus Acer ) have higher N mineralization than neighbouring EGs associated with ECM fungi, but the difference is small to negligible if the DA species are associated with ECM fungi (e.g. species of the genus Quercus). In this case, the magnitude of N mineralization and nitrification would be a question of the particular tree species or genus, rather than a systematic DA versus EG consistent ranking (Wedraogo, Belgy & Berthelin, 1993; Moukoumi et al., 2006; Hobbie et al., 2007; Andrianarisoa et al., 2010). The AM–ECM framework explains an appreciable number of differences in ecosystem functioning due to tree species (Phillips, Brzostek & Midgley, 2013), but it does not appear to be applicable in all cases (Koele et al., 2012) as illustrated by the large fluxes of mineral N produced under the ECM evergreen gymnosperm Pseudotsuga menziesii (Thomas & Prescott, 2000; Zeller et al., 2007; Trum et al., 2011; Mueller et al., 2012b). N mineralization and nitrification depend on many factors and their interactions, e.g. the C:N ratio and N content of dead organic matter, leaf habit and soil acidity (Gower & Son, 1992; Reich et al., 1997a; Scott & Binkley, 1997; Finzi et al., 1998b; Hobbie et al., 2007; Christenson et al., 2009), all variables that are more favourable for N mineralization in DAs (see Sections II and III). However, the amplitude of mineral N production remains difficult to estimate in many cases (Côté et al., 2000; Lovett

L. Augusto and others et al., 2004) and some complex, as yet unexplained, processes play a significant role (Binkley & Menyailo, 2005) resulting in unexpected ranking of tree species for mineralization and nitrification fluxes (Staelens et al., 2012). For instance, land-use history is a factor not frequently taken into account. It is now well established that past land use continues to influence N cycling in soils even centuries after conversion to forest (Compton et al., 1998; Jussy et al., 2002). Different past land uses may have altered the comparison among tree species in some studies. In the same way, the lack of consistency between the results of studies conducted in plantations and those conducted in ‘natural’ forests (Mueller et al., 2012b) suggests that some published studies are actually talking about a difference in ecological niches of tree species rather than a true effect on soil N mineralization. Finally, in situ experiments carried out in common-garden studies with replicated treatments is the most reliable approach to test the tree species effect, but is rarely used (Augusto et al., 2002). Consequently, some published results may have been obtained from not comparable stands, which would have biased the results. All in all, we conclude that specific effects of tree species on soil N fluxes are often large, but no simple factor (e.g. mycorrhizae, leaf habit or tree species group) can account for the important interactions among factors (Priha & Smolander, 1997). These effects could be direct, through inhibitory compounds on soil microbes (Howard & Howard, 1991; Kraus et al., 2003; Smolander et al., 2012), or indirect, through modification of soil properties [e.g. pH or the composition of dead organic matter (Lejon et al., 2005)] or the composition of the understorey (Brierley, Wood & Shaw, 2001), which in turn, partly control microbial activity. These important specific effects require further investigation. Nevertheless, we can conclude that N mineralization and nitrification fluxes are, on the whole, similar or lower under EGs than under DAs. Similarly to N mineralization, the possible influence of spermatophytes on denitrification is not well understood [see e.g. Borken & Beese (2005a)] and requires dedicated studies. Nutrients other than nitrogen have received much less attention from the scientific community. Despite this, it is quite clear that forest overstorey composition influences the cycling of many elements, like silicon (Cornelis et al., 2010), phosphorus (Achat et al., 2013) or calcium (Dauer et al., 2007). The effect of tree species on these cycles is partly controlled by the same biological processes as N because the bioavailability of these elements is partly the result of the mineralisation of organic matter (Dijkstra, 2003; Achat et al., 2013). In addition to these decomposition processes, tree species may modify nutrient cycling through other processes like atmospheric deposition, weathering of soil minerals, saturation in non-acidic cations (K+ , Ca2+ , Mg2+ , etc.) of the soil cationic exchange capacity, or maximum rooting depth (Binkley, 1995; Dijkstra &


Spermatophytes and forest functioning Smits, 2002; de Schrijver et al., 2012). For instance, the more acid and chelating conditions under EG trees may, in strong interaction with the properties of the soil parent material, lead to higher weathering rates of soil minerals (Raulund-Rasmussen et al., 1998; Augusto, Turpault & Ranger, 2000b; Dijkstra et al., 2003) and consequently to higher release of nutrients (e.g. K, Ca, P) from the soil solid phase.

11 V. SYNTHESIS (1) Spermatophytes and forest functioning Larger amounts of elements enter the soil–plant biogeochemical cycle under the influence of EG tree species than DAs (Table 1 and Fig. 2). These larger inputs result from both atmospheric deposition (due to canopy density) and weathering of soil minerals. Conversely, recycling of nutrients appears to be

(2) Forest demand for and use of nutrients Tree species groups, growing within the same climatic and soil conditions, on average produce the same quantity of aboveground litterfall (c in Fig. 1), whereas belowground litter inputs (i.e. fine root turnover) are uncertain. Conversely, the nutrient concentrations in these ephemeral tree compartments are mostly, though not always, higher in DA species than in EG species [e.g. Augusto et al. (2000a), Chabot & Hicks (1982) and Vergutz et al. (2012)] possibly leading to a higher nutrient content (i.e. g-nutrient m−2 ) in one foliage cohort or in fine roots. The annual nutrient demand thus probably differs between the two groups of tree species. Before losing senescing leaves, trees retranslocate nutrients to different extents. Retranslocation efficiency is high for N, P and K, moderate for Mg, and low for Ca (Vergutz et al., 2012). Retranslocation efficiency may differ between DA species and EG species in individual studies (e.g. Son & Gower, 1991; Delucia & Schlesinger, 1995; Eckstein, Karlsson & Weih, 1999), but was not shown to differ, or was very similar, in reviews (Yuan & Chen, 2009a; Vergutz et al., 2012). Consequently, the amount of nutrients required annually for foliage production is generally, but not systematically, lower in EGs than in DAs (Yuan & Chen, 2009b). The fact that leaf life span is generally higher in evergreen species than in deciduous species (van Ommen Kloeke et al., 2012) increases the mean residence time of nutrients in gymnosperm species (Eckstein et al., 1999). Data on temporal dynamics of nutrient contents in fine roots are scarce, but because resorption efficiency is very low in fine roots (Gordon & Jackson, 2000) we hypothesize that the same difference between EGs and DAs as for litterfall may apply to this belowground compartment. Data are also scarce on the relative proportion of the origins of nutrients used for biomass production (i.e. from nutrients stored in trees in autumn versus from soils through root uptake during the growing season). The study of Son & Gower (1991) reported that DAs source three-quarters of their necessary nutrients from their own reserves whereas EGs sourced the same proportion of nutrients from the soil. Along with results on nutrient cycling, this suggests that EG species use less nutrients than DA species, which is an advantage in poor sites (Chabot & Hicks, 1982), but they depend relatively more on soil nutrient pools (Son & Gower, 1991).

Table 1. Summary of the effects of evergreen gymnosperms (EGs) and deciduous angiosperms (DAs) on forest functioning Ecological process Precipitation interception Water throughfalls Water seepage Soil physical properties Soil acidity Forest LAI Production of standing biomass Production of aboveground necromass Decomposition of aboveground necromass Accumulation of forest floor mass Production of belowground necromass Decomposition of belowground necromass Accumulation of organic matter in mineral soils Atmospheric deposition of nutrients Nutrient leaching to surface waters Nitrogen mineralization and nitrification Weathering of soil minerals Tree specific demand for nutrients Nutrient retranslocation efficiency Use of nutrient reserves

Possible influence of tree species groups EG ≫ DA EG ≪ DA EG ≪ DA EG ≈ ?? DA EG > DA EG ≫ ?? DA EG ≥ DA EG ≈ DA EG ≪ DA EG ≫ DA EG ≈ ?? DA EG < ?? DA EG < ?? DA EG ≥ DA (unpolluted area) EG ≫ DA (polluted area) EG ≥ DA (unpolluted area) EG ≫ DA (polluted area) EG ≤ ?? DA EG ≥ DA EG ≤ DA EG ≈ DA EG < ?? DA

≫, >, and ≈ indicate an average difference of more than 30, 10–30, and less than 10%, respectively. These differences are general trends based on average values; exceptions will exist depending on individual tree species and environmental conditions. ≥ indicates a difference as a general trend, but with a non-negligible proportion of counter-examples. ?? indicates a speculation based on scarce data which requires further investigation. For instance, leaf area index (LAI) difference is based on only seven case studies (see Fig. 1) involving mainly Picea spp. as EG replicates. In this example, including additional Pinus species (known to have quite open canopies) may remove the difference.


12


Fig. 2. Functioning scheme summarizing the differences in biogeochemical cycles between evergreen gymnosperm forests and deciduous angiosperm forests. Small tree icons indicate a higher flux (or stock) for this group of tree species compared to the other spermatophyte type (oval canopy = DA; triangle canopy = EG).

enhanced in DA forests, as shown by higher nutrient fluxes through production and decomposition of dead organic matter (Table 1; Fig. 2). Biogeochemical cycles of nutrients in EG forests may be described as more representative of an acquiring strategy than in DA forests. Reciprocally, DAs may be qualified as having a more recycling strategy than EGs. This scheme is of course only relative as all tree species both acquire and recycle nutrients, and should not be interpreted in a simplistic way. The main drivers of these differences are tree architecture, tissue properties and mycorrhizal associations, which modify soil acidity, the quality of soil Corg and the composition and activity of soil fauna and microbial communities. Results of a few studies suggest that the influence of trees on acidification and nutrient cycling are not completely interdependent. Studies of forests growing on very different soils showed that the foliage content in nutrients or lignin for a given species is only moderately modified by soil properties [coefficient of variation = 5–35% for lignin, N, P, K, Ca, or Mg content; 40–65% for Mn or Al content (Vesterdal, 1999; Meier, Leuschner & Hertel, 2005; Sariyildiz & Anderson, 2005)]. This suggests that tree chemistry may be less controlled by the nutrient supply from the soil than by species-specific processes of homeostasis (Smith & Shortle, 2001; Dauer et al., 2007). The differences in foliage and litter chemistry between DAs and EGs thus might be more intrinsic than a consequence of

the site characteristics or of any feedback induced by soil acidification or Ca uptake from deep soil layers [see Berger et al. (2006)]. In this view, the positive correlation between Ca and Mg fluxes through litterfall and soil stocks of Ca2+ and Mg2+ reported by Langenbruch, Helfrich & Flessa (2012) would be a consequence of the tree-species-specific chemistry effect on soil rather than the reverse. In other words, the EG or DA imprint on ecosystem functioning may be observed in wide ranges of sites with different properties. Consequently, a shift of tree species niches towards higher latitude/altitude (Cramer et al., 2001) would notably modify the ecological functioning of many sites. However, as shown above, this imprint will probably not be a one-way relationship because the influence of EGs or DAs interacts with many factors. For instance, foliage content in manganese (Mn) is highly dependent on site characteristics (see Section V.1) because plants do not appear to have complete control of its uptake (Marschner, 1995). The bioavailability of Mn in soils increases with an increase in soil acidity (Meier et al., 2005; Langenbruch et al., 2012), which is consistent with the high Mn concentration in the foliage of EGs and their ability to acidify soils. This example confirms that to understand the consequences of tree species migration under global change we need to take not only the direct plant–soil relationship into account but also the way it is partly modified by other factors (see Section V.3).


Spermatophytes and forest functioning (2) Understorey: an example of cascade effects In the above sections, we mainly focussed on direct effects of trees on ecosystem functioning. It should be highlighted that EGs and DAs can, in addition, indirectly influence the ecosystem by modifying its biological composition. For instance, tree species can affect soil communities of earthworms which, in turn, change litter decomposition dynamics (see Section III.3). Less-known cascade effects are due to plants in the forest understorey. Although EG and DA forests, when in the same environment, have similar richness in plant species (Crozier & Boerner, 1984; Qian, Klinka & Sivak, 1997; Verstraeten et al., 2013), they often have different understorey composition (Augusto et al., 2003; Verstraeten et al., 2013). In temperate and boreal forest, late successional EG species (like some Abies or Picea species) enhance the abundance of mosses and reduce the abundance of herbaceous species compared to DA species (Augusto et al., 2003; Cavard et al., 2011). This shift in abundance is likely a consequence of forest microclimate. Indeed, the shadier and drier conditions in such EG stands (see Section III.3) tend to limit the development of herbaceous communities (Økland, Rydgren & Økland, 1999; Légaré et al., 2005). Conversely, air humidity may be higher in some EG stands than in DA stands (Nihlgård, 1969) providing favourable growing conditions for mosses (Frisvoll & Presto, 1997). Understorey composition, however, interacts strongly with forest management (Hill & Jones, 1978; Brunet et al., 1997; Augusto et al., 2003; Verstraeten et al., 2013) and, to a lesser extent, with EG tree species (Klinka et al., 1996). A thick moss layer may not be observed in some EG forests, such as frequently thinned stands. In forests where a large and thick stratum of mosses is present, it modifies many processes of the ecosystem. Because mosses have no root (only short rhizoids), no or limited cuticle, and very high SLA, they rely mainly on atmospheric inputs for their water and nutrient supply. Almost all absorbed water, and the majority of absorbed nutrients, are provided by direct precipitation or forest throughfall; only the very top of the soil is exploited (Anderson & Bourdeau, 1955; Binkley & Graham, 1981; Weber & Van Cleve, 1984; Startsev et al., 2008). Moss may thus act as an intercepting layer for water and nutrients, reducing their movement and recycling. Indeed, due to low N content, an acidic nature, and high content in secondary metabolites, dead mosses decompose slowly and dead organic matter may accumulate (Aerts, Verhoeven & Whigham, 1999; Scheffer, van Logtestijn & Verhoeven, 2001). Such accumulation may delay nutrient recycling (Bates, 1989; Brown & Bates, 1990; Eckstein, 2000; DeLuca et al., 2008; Fenton, Bergeron & Paré, 2010). Similarly, soils under a thick mantle of mosses are characterized by buffered temperature amplitudes and a modified water regime compared to soils under a forest floor (Startsev, Lieffers & McNabb, 2007; Fenton

13 et al., 2010). Finally, in unpolluted regions where N atmospheric deposition is low (Zackrisson et al., 2009; Leppänen et al., 2013), some mosses species symbiotically associated with cyanobacteria fix notable quantities of atmospheric N2 (DeLuca et al., 2002, 2008). As symbiotic fixation of this kind of mosses is independent of tree litter type [i.e. EG or DA; Gundale, Gustafsson & Nilsson (2009)], N2 -fixation flux may be higher in a boreal EG stand compared to a neighbouring DA stand. The example of mosses illustrates well how tree species can indirectly modify ecosystem functioning through companion species (Hansson et al., 2013a). (3) Knowledge gaps Our review of the literature is largely based on tree species of the Betulaceae, Fagaceae, and Pinaceae families (see online Table S1, Appendix S1), reflecting the fact that most studies have been carried out in Northern America and Europe. It implies that our conclusions are well supported in the context of the Northern hemisphere, but are based on a limited number of studies for the Southern hemisphere. This is the case for gymnosperms which are mainly represented by the Cupressaceae and the Podocarpaceae families in the Southern hemisphere (Brodribb et al., 2012). Our compilation did not show any obvious inconsistency between these lineages and the Pinaceae (e.g. Hoorens et al., 2010). However, some differences do exist. For instance, Pinaceae species are associated with ECM fungi whereas Podocarpaceae species are AM (Brodribb et al., 2012). Because the type of mycorrhiza has an influence on forest functioning like soil particle weathering (Taylor et al., 2009), N mineralization (Phillips et al., 2013), soil C:N ratio (Averill, Turner & Finzi, 2014), or nutrient uptake (Read & Perez-Moreno, 2003; Plassard & Dell, 2010), general patterns based on Pinaceae species may be not applicable to other gymnosperm families (Quirk et al., 2012). Consequently, further investigations on austral tree species [e.g. Enright (2001) or Jackson et al. (2013)] are required to confirm the conclusions of the present study, provided they are based on reliable experimental designs such as replicated common-garden approaches (e.g. de Schrijver et al., 2012). The influence of tree species on soil biogeochemistry, physical–chemical properties and biological processes and communities are mainly visible in the forest floor and in the topsoil (Challinor, 1968; Binkley, 1995; Dijkstra & Smits, 2002; Augusto et al., 2003; Dijkstra, 2003; Cornelis et al., 2011; Hansson et al., 2011; Langenbruch et al., 2012). Differences in aboveground functioning between EG and DA forests are likely to be, at least partly, at the origin of such a pattern. However, plant fine roots are also mostly located in the topsoil volume (Jackson et al., 1996) and differences in belowground functioning may play an important role (Wardle et al., 2004; Clemmensen et al., 2013). As repeatedly highlighted in our review, belowground processes are poorly


14 understood. This lack of knowledge disables proposing any solid and general conclusion on tree species effects. Consequently, research on belowground processes in forests is urgently needed, not only to compare tree species, but to understand forest ecosystems better. The effects of the type of functional systematic group (like EGs and DAs), and of individual tree species, strongly interact with several factors, including climate, time since the change of tree species and soil intrinsic properties or management, while site properties often dominate (Augusto et al., 2001b; Dijkstra et al., 2003; Binkley & Menyailo, 2005; Ladegaard-Pedersen et al., 2005; De Santo et al., 2009; Guckland et al., 2009; Berger, Inselsbacher & Zechmeister-Boltenstern, 2010; Chodak & Niklinska, 2010; Homann, 2012; McIntosh, Macdonald & Gundale, 2012; Prescott & Grayston, 2013; Vesterdal et al., 2013). These numerous interactions may be difficult to address and some have not been investigated using well-designed experiments. For instance, after a tree species substitution, does the observed ecosystem functioning reflect the long-term sustainable effect of the new species or a transition period towards a new stabilized state? Can this explain unexpected patterns, like very high rates of N mineralization in some Pseudotsuga menziesii forests? Another example is plant species–site interactions, an important scientific field that remains insufficiently addressed (Eviner & Hawkes, 2008). Indeed, ranking of tree species, and even the processes explaining the results, may drastically change with site conditions, like soil type (e.g. Ste-Marie, Pare & Gagnon, 2007; De Santo et al., 2009; Keiser, Knoepp & Bradford, 2013; Rinkes et al., 2014). In some cases, the response is probably related to plant growth rate. If, in a given site, a tree species has a much higher rate of biomass gain than another tree species, then its footprint on ecosystem functioning would be deeper because of an effect size of, for instance, carbon (Gurmesa et al., 2013) and nutrient fluxes (Augusto et al., 2000a). However, in many cases plant–site interactions cannot be fully explained by such a simple interpretation (Rothe et al., 2002b; Ladegaard-Pedersen et al., 2005). In the context of global change, providing average conclusions about the effect of groups of tree species, like EG and DA species, is not enough because practical decisions should be taken based on local conditions (Eviner & Hawkes, 2008). Consequently, there is an urgent need to understand how the influence of certain tree species is modulated (e.g. Augusto et al., 2001b; de Schrijver et al., 2007) by site conditions. Another open question is how long the legacy of prior species lasts. In our opinion, particular attention should be paid to some biotic interactions which may be highly complex but can significantly modify ecosystem functioning like litter–fauna interactions (e.g. Hättenschwiler & Gasser, 2005), possible allelopathic effects between different organisms including plants and microbes, or other possible feedback loops caused

L. Augusto and others by a change in flora (Barbier, Gosselin & Balandier, 2008; Cavard et al., 2011) or fauna (Deharveng, 1996; Felton et al., 2010) triggered by tree species. An important process involved in forest functioning that should be addressed is the question of the home field advantage. This term refers to the fact that dead foliage may decompose more rapidly under the same species that produced the litter (Gholz et al., 2000; Vivanco & Austin, 2008; Jacob et al., 2010; Prescott, 2010; Keiser et al., 2013; Prescott & Grayston, 2013). Because the presence of plants has no direct effect on litter decomposition (Trinder, Johnson & Artz, 2009; Coq et al., 2011), it has been assumed that this result was a consequence of soil biota specialized in decomposing the dominant leaves of the forest floor (Binkley, 1995). Nevertheless, the home field advantage is not systematic (Trinder et al., 2009) as it may be influenced by local site conditions like N bioavailability (Weand et al., 2010; Vivanco & Austin, 2011). More generally, the importance of the specific composition of the forest floor raises questions about the possible role of mixtures of tree species in forest functioning (Forrester, Bauhus & Cowie, 2005; Kelty, 2006; Langenbruch et al., 2012) because many forest ecosystems have more than one tree species. In our opinion, future investigations should focus on disentangling the individual factors that control possible interaction effects. Indeed, species diversity effects may simply be the result of the proportional influence of each tree species (Hojjati et al., 2009; Hoorens et al., 2010). In this case, there is no interaction but only additive effects. Conversely, some significant interaction effects, i.e. non-additive effects (synergistic effects or antagonistic effects), have been reported from mixed forests (Schume, Jost & Hager, 2004; Knoke et al., 2008; Richards et al., 2010; Coq et al., 2011; Morin et al., 2011; Berger & Berger, 2012; Kiikkilä et al., 2012; Keiser et al., 2013; Li & Liu, 2013). The existence, or absence, of non-additive effects is a major issue if we aim to manage forest ecosystems more efficiently. (4) Tree species as mitigation tools? The fact that the influence of tree species on ecosystem functioning is itself deeply influenced by interactions with factors such as climate or the chemical composition of the atmosphere suggests that global change is very likely to modify some patterns. Indeed, due to climate change, tree species are expected to migrate to higher latitudes and altitudes (e.g. Cramer et al., 2001). Similarly, EGs and DAs are expected to differ in terms of growth response to the current increase in atmospheric CO2 (Saxe, Ellsworth & Heath, 1998). All these impacts of global change need to be addressed if we aim to adapt forest ecosystems. On the other hand, we should also think about the possible role of tree species as mitigation tools. It has been known for decades that ecosystem acidification was greatly enhanced by uncontrolled


Spermatophytes and forest functioning air pollution in industrial countries (Ahokas, 1997; Drohan & Sharpe, 1997; Oulehle, Hofmeister & Hruska, 2007). Because DAs capture less atmospheric deposition than EGs, they could be recommended in emerging economies like Southeast Asia where atmospheric pollution can be very high (Lu & Tian, 2007). In the same way, a strategy to store carbon in forests consists of enhancing the incorporation and stabilization of carbon in mineral soil (Jandl et al., 2007; Prescott, 2010; Bellassen & Luyssaert, 2014). Based on the literature, we suggest that DAs may enhance these processes in moderately acidic sites. If this hypothesis is confirmed, it would be possible to increase long-term soil carbon storage by promoting the use of hardwood species. Conversely, in sites where conifers are more productive than hardwood species (Pretzsch, 2009), the former could help store carbon in the form of wood products (Lindner & Karjalainen, 2007), although other aspects than carbon storage must also be taken into account in forest management (e.g. timber quality and its use). However, here again, general trends interact with context and simplistic statements must be assessed locally before being applied (Eviner & Hawkes, 2008). In addition to management for carbon sequestration, the choice of tree species may influence water management at the watershed scale (Swank & Vose, 1994). If the goal is to maintain the supply of water, DAs should be preferred to EGs as the latter reduce water discharge. Conversely, if the aim is to reduce flood occurrence and intensity, EGs may be promoted (Swank & Vose, 1994).

VI. CONCLUSIONS (1) Our review of the literature indicates that forests composed of DAs function with notable differences compared to EG forests. (2) EG forests intercept more precipitation than DA forests, leading to drier soil conditions, and lower water discharge to surface waters and ground water reserves. (3) Fluxes and stocks of carbon are comparable in DA and EG forests, but some differences exist between the two groups of species for tree growth, forest floor accumulation and carbon stabilization in mineral soil. (4) Ecosystem nutrient cycling is generally higher in DA forests than in EG forests in which input–output fluxes are often higher. (5) It should be underlined that the above differences are general trends based on mean values. Locally, other patterns of ranking can exist because of different sources of variability or uncertainty. (6) Functional traits are highly variable among tree species of a given group. (7) Forest functioning depends on multiple and complex interactions with many environmental factors. Such interactions can change the ranking within a

15 defined pair of tree species and tree species effects on forest functions are consequently context-dependent. (8) Knowledge is lacking on certain lineages, such as the most common gymnosperm families in the Southern hemisphere (e.g. Podocarpaceae). (9) Based on our synthesis we conclude that a tree species composition of either DAs or EGs implies a type of biogeochemical functioning in a forest ecosystem. Nevertheless, scientists and foresters should remember that this conclusion is based on general trends, and not on systematic rules which cannot encompass the complexity of nature.

VII. ACKNOWLEDGEMENTS We sincerely thank Mark Bakker, Sylvain Delzon, Mathieu Fortin, André Schneider and especially Dan Binkley for their encouragement, advice, and pertinent comments on previous versions of this article. We also thank two anonymous reviewers who helped us improving the present article.

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IX. SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article. Appendix S1. Overview of forest functioning. Appendix S2. Methods used to create Fig. 1.

(Received 26 April 2013; revised 25 March 2014; accepted 28 April 2014 )


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