DOI: 10.5061/dryad.n02v6wwt7
title: Tree-to-tree interactions slow down Himalayan treeline shifts as inferred from tree spatial patterns

description: Aim: The spatial patterns of tree populations reflect multiple ecological
 processes. However, little is known whether these patterns mediate
 responses to climate in marginal tree populations as those forming alpine
 treelines. Harsh conditions at these ecotones imply the existence of
 positive interactions which should lead to tree clustering. In fact,
 densification in response to climate warming is more widely reported than
 upward shifts in most treelines. This suggests that more intense
 tree-to-tree interactions could buffer the treeline responses to climate
 warming, resulting in low treeline shift rates. Location: Central
 Himalayas. Methods: We examined influence of tree-to-tree interactions on
 the responsiveness of treelines to climate warming by analyzing a network
 of 17 treeline sites located across the central Himalayas, and
 encompassing a wide longitudinal gradient characterized by increasing
 precipitation eastwards. We quantified the changes in density and the
 spatial patterns of three 50-year age classes of the two main tree species
 found at treeline (Betula utilis and Abies spectabilis), and related them
 to reconstructed shifts in treeline elevation. Results: Young trees showed
 clustering near the treeline, while older trees tended to show random
 spatial distribution. Clustering decreased as climate conditions
 ameliorated, i.e. in the wetter eastern sites. A negative association
 between upward treeline shift rate and clustering indicates that tree
 aggregation weakens treeline responsiveness to climate warming. Thus,
 warming-induced drought stress tends to lower treeline shift rates by
 enhancing clustering. Main conclusions: Our results highlight the
 complexity and contingency of site-dependent treeline responses to
 climate. Hence, to advance our understanding on treeline processes, we
 should consider both direct and indirect influences of relevant biotic
 (tree-to-tree interactions) and abiotic (climate) drivers of treeline
 dynamics.
description: 17 rectangular plots at treeline ecotone with a size 30 m × 150 m
 (except M1, 30 m × 100 m) were established on gentle slopes ranging from
 12º to 22º, and placed away from previously affected areas by landslides
 or avalanches. All plots included the upper treeline and the forest limit
 which was defined as the elevation of closed forest with at least 30 %
 canopy cover (Camarero & Gutiérrez, 2004; Liang et al., 2011;
 Liang et al., 2016). The plots had its longer side (<i>y
 </i>axis) located parallel to the elevation gradient. For all plots,
 coordinates (<i>x, y</i>) = (0, 0) were situated in the left,
 bottom corner (Camarero & Gutiérrez, 2004). The geographic
 information of all four corners was noted by using a Global Positioning
 System (GPS) with ± 4 m accuracy. The location of each individual of
 targeted species was based on the x, y reference points. Coordinates were
 measured to the nearest 0.1 m (Liang et al., 2016). To quantify the
 spatio-temporal patterns of Himalayan treelines, the changes in tree
 density and treeline elevation were calculated for 50-year age-classes
 (1-50 yrs., 51-100 yrs., and 101-150 yrs.). We used the “spatstat” package
 (Baddeley, Rubak, & Turner, 2015) in R program (R Development Core
 Team 2018) to calculate nearest-neighbour (NN) distances for all trees
 grouped in the three age classes. To summarize the spatial distribution
 patterns of each age-class using a simple measure, we used the
 <i>R</i> statistic (clustering intensity) calculated in PAST
 software (Hammer, Harper, & Ryan, 2001). If a Poisson random
 process and an exponential NN distribution are assumed,
 <i>μ</i>  may be defined as: <i>                                               </i>                                                                              (1) where <i>A</i> is the area and <i>n</i> the number of trees. Then, the <i>R</i> statistic is calculated as: <i> </i>                                                                  (2) A spatial pattern may be described as clustered, random (Poisson) or overdispersed for <i>R </i>&lt; 1, <i>R </i><b>~<sup> </sup></b>1, and <i>R </i>&gt; 1, respectively (Clark & Evans, 1955).