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Tree-to-tree interactions slow down Himalayan treeline shifts as inferred from tree spatial patterns

Citation

Sigdel, Shalik Ram et al. (2021), Tree-to-tree interactions slow down Himalayan treeline shifts as inferred from tree spatial patterns, Dryad, Dataset, https://doi.org/10.5061/dryad.n02v6wwt7

Abstract

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.

Methods

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 (y axis) located parallel to the elevation gradient. For all plots, coordinates (x, y) = (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 R statistic (clustering intensity) calculated in PAST software (Hammer, Harper, & Ryan, 2001). If a Poisson random process and an exponential NN distribution are assumed, μ  may be defined as:

                                                                                                                             (1)

where A is the area and n the number of trees. Then, the R statistic is calculated as:

                                                                   (2)

A spatial pattern may be described as clustered, random (Poisson) or overdispersed for R < 1, R ~ 1, and R > 1, respectively (Clark & Evans, 1955).

Funding

Second Tibetan Plateau Scientific Expedition and Research Program (STEP), Award: 2019QZKK0301

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDA20050101

National Natural Science Foundation of China, Award: 41525001, 41661144040

Chinese Academy of Sciences President’s International Fellowship Initiative, Award: 2018PC0040