^{18}O, δ^{2}H) as hydrological marker.

^{2}). We tested the hypothesis that both species uptake water differentially along the soil profile, thus showing different levels of tree-to-tree interdependency, depending on whether neighbouring trees belong to one species or the other.

^{18}O = –5.3 ± 0.2‰, δ^{2}H = –54.3 ± 0.7‰) were significantly lower than for ^{18}O = –1.2 ± 0.2‰, δ^{2}H = –25.1 ± 0.8‰), pointing to a greater contribution of deeper soil layers for water uptake by

^{18}O, oxygen isotope composition

^{2}H, hydrogen isotope composition

The authors have declared that no competing interests exist.

In Mediterranean climates, the temporal coupling of heat and drought stress, and the existence of nutrient-deficient soils have been major evolutionary forces shaping plant communities (

A considerable number of studies have shown how inter- and intra-specific competition affects individual growth and stand dynamics under water-limited conditions (^{18}O and δ^{2}H) in xylem sap presents a great potential to characterise water movement along the soil-plant-atmosphere continuum, particularly in arid and semi-arid environments (^{18}O and δ^{2}H with soil depth (

Forest science has applied numerous statistical methods belonging to point processes (^{18}O and δ^{2}H records of xylem water. Due to their deeper root system, oaks are likely to extract water from soil layers not accessible for the pines. We hypothesize that, under drought conditions, the two species might not directly compete for the same water pools in the soil, thus showing a functional niche segregation. Accordingly, we would expect different levels of tree-to-tree interdependency, depending on whether neighbouring trees belong to one species or the other.

The study area is a forest stand located in the Montsant mountain range (41° 19’ 47.3’’ N, 0° 50’ 2.6’’ E, 750 m a.s.l), in the northeast of the Iberian Peninsula. The climate in the region is Mediterranean temperate with continental tendency, with a mean annual precipitation of 517 mm and mean annual temperature of 12.3 °C. It is characterized by a dry and a relatively warm summer (mean summer precipitation of 89.5 mm, mean average temperature of 20.9 °C; averaged data of the two nearest meteorological stations with a long-term record (period 1970-2000),

The rectangular plot area (24 x 37 m) had a strong slope (15-22%) facing west (X-axis), together with a gentle slope (3-7%) facing south (Y-axis). According to USDA soil taxonomy (

Field sampling took place on the 9^{th} September 2011, at the end of an exceptionally dry, but moderately warm, summer (summer precipitation of 23 mm, mean summer temperature of 21.5 °C, data from ^{18}O, and hydrogen, δ^{2}H). Raw values were calibrated against three internal laboratory references (calibrated against IAEA standards VSMOW2, SLAP2 and GISP). Overall uncertainty (determined as the standard error of repeated analyses (^{18}O and δ^{2}H, respectively. The potential presence of organic contaminants was checked using the post-processing software Picarro ChemCorrect 1.2.0, giving in all cases negative results.

Tree position for spatial analysis was determined using a high resolution GPS technology (GeoExplorer 6000 Series Handheld, Trimble Navigation Limited, California, USA) with spatial error inferior to 20 cm for latitude and longitude and to 40 cm for altitude. Tree coordinates were re-checked in the field with the aid of a measuring tape.

Isotope data (δ^{18}O and δ^{2}H) and one tree dendrometric characteristic (individual basal area, BA) were subjected to mixed model analysis of covariance (ANCOVA) considering a fixed effect for species (pine, oak) and the variation along the X and Y axes of the two-dimensional space (covariates), allowing for heterogeneity of regression slopes at the species level. This was done to check for (possible) differential systematic variation in the response variables following X and Y directions, i.e. anisotropic effects. We also allowed for heterogeneity of residual variances at the species level, which was checked by means of log likelihood ratio tests. For the difference between two nested models (homocedastic and heterocedastic), minus two times the log likelihood ratio follows, under the null hypothesis, asymptotically a χ^{2} distribution with one degree of freedom (difference in the number of variance components;

To analyse the spatial structure of _{i} in a bounded region

To study the spatial structure of trees (point locations) we used the pair correlation function (

for a forest stand _{1} ≠ _{2} and

Broadly speaking, this function indicates point inhibition (i.e. repulsion) when

To analyse the bivariate point pattern of _{12}(_{12}(_{12}(_{12}(_{12}(

where _{s} and _{12} = _{1} ∪ _{2}, i.e. the bivariate point pattern.

To analyse the marked point patterns of oaks and pines, we used the mark correlation function _{m}(

where _{m} is a marked point pattern, ^{2} is the expectation of _{1}
_{2} and _{1} is the mark value for tree 1 (say). This function denotes independence between marks when _{m}(_{m}(_{m}(

where _{m}_{1} is the marked point pattern for class 1 (say), and _{12} is an estimator of _{12}, is the expectation of _{1}
_{2} (marks from classes 1 and 2). The interpretation of _{m}(_{12}(

For each kind of spatial correlation function, we tested for spatial independence following a Monte Carlo approach based on the random simulation of (marked) point patterns from the null hypothesis (Poisson). We simulated 199 (marked) point patterns under the null hypothesis of spatial independence, and for each one, an estimator of one of the correlation functions defined above was obtained. These set of functions were then compared with the resulting estimator of this correlation function for the point pattern under analysis. Under this test, we rejected the null hypothesis (spatial independence) if the resulting estimator of this correlation function lay outside the fifth largest and/or smallest envelope values obtained from the set of simulated functions with an exact significant level of α = 2 × 5 / (199 + 1) = 0.05. Tests for each (marked) point pattern considered here are defined as follows. For the point patterns of oaks and pines analysed separately we tested against spatial point independence based on the random simulation of Poisson point configurations (see for instance,

For the statistical analysis of point patterns, we considered the computational implementation in the statistical package Spatstat for the R statistical environment (

The analysis of isotopic compositions of water extracted from soil samples showed a decreasing trend along the soil profile. In particular, the topsoil was significantly more enriched (δ^{18}O = 0.2±1.2 ‰; δ^{2}H = 34.6 ± 3.8 ‰) than the subsoil (δ^{18}O = -3.0 ± 2.4 ‰; δ^{2}H = –45.4 ± 8.5 ‰) (^{18}O and δ^{2}H, respectively; two-tailed, paired ^{18}O = 20.3–2.2 × depth(m), ^{2} = 0.85, ^{2}H = 3.1 – 0.52 × depth(m), ^{2} = 0.67, ^{18}O = –7.4 to + 3.0‰ and δ^{2}H = –61.5 to –7.0‰ in pines; δ^{18}O = –8.1 to –2.6‰ and δ^{2}H = –67.8 to –41.6‰ in oaks) was comparable, although in some cases exceeded the range observed in soil samples, particularly for δ^{2}H (δ^{18}O = –5.7 to + 1.7 ‰; δ^{2}H = –55.7 to –28.5‰).

ANCOVAs revealed significant differences between pines and oaks (–1.2 ± 0.18‰ and -5.3 ± 0.15‰, respectively, for δ^{18}O; –25.1 ± 0.78‰ and –54.3 ± 0.66‰, respectively, for δ^{2}H), in addition to a progressive increase of both isotopes along the X axis that was significantly higher for ^{–1} and 0.155‰ m^{–1}, for pines and oaks, respectively, for δ^{18}O; 0.248 ‰ m^{–1} and 0.610 ‰ m^{–1}, for pines and oaks, respectively, for δ^{2}H) (test of unequal slopes;

The spatial locations of

Visual inspection of the mark point pattern of

^{18}O residuals and the resulting mark correlation function, highlighting that water uptake strategies for

As expected, comparable results to those of δ^{18}O were obtained for the spatial structure of δ^{2}H residuals (^{2}H residuals had spatial dependencies at short inter-tree distances (<4 meters), while for

Marked point process statistics are valuable techniques to evaluate and describe forest systems (see, amongst others, ^{18}O and δ^{2}H) was on average significantly higher in Aleppo pine than in Holm oak. Decreasing trends in soil water δ^{18}O and δ^{2}H were also observed with soil depth, confirming the existence of an evaporative gradient in the soil. An increasing trend in xylem water δ^{18}O and δ^{2}H was also observed along the X dimension of the experimental plot, which agrees with decreasing soil depth following this direction, hence favoring higher water evaporation. However, this trend was steeper for ^{18}O and δ^{2}H of xylem water with the soil profile, we may first conclude that Holm oak takes up more water from deeper soil layers than Aleppo pine after a long drought period, as would be expected according to the deeper root system of evergreen schlerophyllous, as compared to pines (^{18}O and δ^{2}H of xylem water), and an uncorrelated spatial configuration for oaks (see

Similarly, the mark correlation function for BA of pines (

In any case, it is likely that the competitive effect of Holm oak trees on individuals of Aleppo pine was much lower than if neighbor trees were from the same species. Particular reasons for this may be two-fold. On the one hand, and regarding competition for water resources, the observed evidences of distinct water uptake patterns for the two species may explain the lack of interaction, even when water resources are limiting (see e.g.

In this study we initially assumed that differences in xylem water would reflect distinct water uptake patterns originating from contrasting contributions of soil layers.

However, whereas interspecific differences can be easily explained by the uptake of water from different depths, the observed increase in δ^{18}O and δ^{2}H in neighbouring trees, particularly in pines, is less straightforward. The presence of close neighbours can be interpreted as a local increase in stand density, and indeed more positive values in δ^{18}O of xylem water of Aleppo pine have been reported when comparing a densely afforested stand (770 trees ha^{–1}) with an open woodland (20 trees ha^{–1}) (^{18}O and δ^{2}H observed in trees with close neighbours. This would also explain the stronger neighbour effect in pine as compared to oak, since the former is a water-saving species, with a more sensitive stomatal response (

Although results from our case study are not totally conclusive, the application of point-process statistical tools has allowed us to go beyond the comparison of inter and intra-specific (non-spatial) differences in water uptake, thereby revealing complex spatial dependencies in the use of water. In particular, our study indicates complementary water uptake patterns between Aleppo pine and Holm oak during the dry season, showing intra-specific competition among neighbour pines, but neither facilitation nor competition between individuals of different species. These results, however, might not be extrapolated to any pine-oak mixed stands, since root development might be affected by the history of the stand (e.g. whether oaks are seedlings or sprouts) and the different degree of dominance of each species. However, it should be noted that competition for water resources can be dynamic, mainly modulated by water availability (see e.g.

We gratefully acknowledge the assistance of JR Olarieta in soil taxonomy classification.

The authors acknowledge M.J. Pau and P. Sopeña for technical assistance.