Age–size–habitat relationships for Polylepis australis: dealing with endangered forest ecosystems

Abstract

Assessing tree ages is important for the understanding of forest recruitment patterns and tree growth. However, little experience exists in the subtropics and accurate aging usually involves potentially damaging techniques such as tree coring or even the use of entire cross sections, which is not recommendable in endangered species or ecosystems. We provide an example of how age may be predicted on the basis of tree size and site conditions, using Polylepis australis of Central Argentina. Our study was conducted in two entire river basins where 96 independent trees were successfully cored for age analysis. Site and tree characteristics were registered and tree age determined through standard dendrochronological techniques. The multiple regression procedure selected tree circumference and proportion of rock under the tree canopy as signiWcant variables explaining 48% of the variation in number of growth rings. Trees surrounded by rock clearly grew slower than those surrounded by a higher proportion of soil and vegetation. A comparison with a one site study shows that the ability to predict P. australis age is clearly reduced when geographical range is extended but the explanatory power of our model is still high enough for certain applications and within the range of other one site studies. We suggest that tree size and site characteristics may be used to predict age in other subtropical mountain forests with a well marked growth season.

Allometry and effects of extreme elevation on growth velocity of the Andean tree Polylepis tarapacana Philippi (Rosaceae)

“…Even though a study addressing the possibilities of using morphometrics to predict age in P. australis revealed a close relationship between number of treerings and circumference (Suarez et al 2008), our results did not show any relationship for P. tarapacana. We suggest that P. tarapacana tends to invest more resources to increase diameter faster than height as a strategy to reduce stem buckling, especially by wind storms, as an additional adaptation to extreme elevation.…”

Historic and recent fragmentation coupled with altitude affect the genetic population structure of one of the world's highest tropical tree line species

“…Sampled adult trees were separated by at least 20 m to minimize the chance of sampling closely related or genetically identical individuals. Given the size of the adults and estimated growth rates for other species of the genus (Suarez et al ., 2008), we assume that the majority of individuals were > 100 years old. Leaves were stored in bags with silica gel prior to analysis.…”

Landscape Structural Complexity of High‐Mountain Polylepis australis Forests: A New Aspect of Restoration Goals

“…For each plot we recorded: (1) percentage of P. australis canopy cover (visual estimation, projection of canopy cover); (2) height of all living P. australis individuals taller than 30 cm; (3) basal circumference of the largest tree and proportion of rock under its canopy—both to estimate the tree age following Suarez et al (2008); (4) standing; and (5) fallen dead volume of wood by measuring length and average circumference; (6) visual estimation of the proportional fern cover (%); (7) density of Maytenus boaria individuals (the only tree species accompanying P. australis ) taller than 30 cm; (8) altitude above the sea level; (9) topographic position in three categories: (i) valley bottoms and lower slopes (hereafter called valley bottoms), (ii) mid‐slopes, and (iii) upper‐slopes and convex summits (hereafter called upper‐slopes); (10) slope inclination with a clinometer (RECTA SA, CH‐2501); (11) slope aspect with a compass (RECTA SA, CH‐2501); (12) sun incidence at the canopy level measured as the trajectory of the sun not covered by mountains, rock outcrops, or other obstacles as calculated with the clinometer (in a flat area with no obstacles: 180°).…”

“…The index of dead wood volume (IDWV) was calculated as IDWV = hC 2 /4π, where h is the length and C the average circumference. Age of the largest tree was estimated as: Log(age in years) = −0.16 + (0.85 × log (circumference in cm)) + (0.0013× % rock under the tree canopy), which in our study area explains 48% of variation in age over a wide range of habitat types (Suarez et al 2008).…”