Patterns of tree species richness in Jalisco, Mexico: relation to topography, climate and forest structure

Abstract

Abstact The objective of this study was to identify the major environmental variables and components of forest structure associated with variability in tree species richness on a network of 806 permanent plots in the State of Jalisco, Mexico. Tree data recorded on the sample plots were used to characterize tree species richness by forest type and climatic conditions (temperature and precipitation) in the State. Species composition and other diversity indices were also calculated. Explanatory variables identified in a Poisson regression identified forest cover type, elevation, tree basal area, canopy closure, and winter precipitation as being important to changes in tree species richness. An ''extreme quantile curve estimation'' approach was then used to approximate the boundary that represented the maximum potential species richness response to the various levels of important variables. Maximum tree species richness decreased with increasing elevation. The relationships between maximum species richness and tree basal area, canopy closure, and winter precipitation followed a hump-back unimodal model, with intermediate values supporting the largest species richness. We believe that results of the current study will contribute to further development of a conservation plan for tree species in the State of Jalisco, Mexico.

Tamaño De Fragmentos Forestales en El Bosque De Pino-Encino, Sierra De Quila, Jalisco

“…Si bien, las It is observed that the higher values in tree density and average diameter i n BC fragments are relevant and can affect the local variability of species richness. This agrees with some structural patterns detected by Reich et al (2010) which stat that the tree species richness of Jalisco state can be explained by the kind of forest cover, altitude, tree basal area, canopy cover and winter rainfal l.…”

“…The time-space patterns of vegetation have been interpreted as a result of the influence of two kinds of gradients: a) the limiting environmental factors such as light, temperature, water, soil nutrient, for example; and b) the natural or human disturb (Jardel et al , 2004). For example, the topographic variation (altitudinal gradient) and the several types of soil are physical variables taken into account in the general hypothesis, that help to explain the species diversity at local geographic scales used in other mountain ecosystems in Mexico (Jardel et al , 2004;Cuevas et al , 2008;González-Espinosa et al, 2008;Reich et al, 2010).…”

“…The combination of biological, environmental, topographic and climatic data represents a complex gradient in which multiple variable are involved; thus, to explain the patterns or tree species richness and associations it is basic to include different time and spatial scales of the physical environment, as well as the series of limiting factors and ecophysiological elements of taxa (González-Espinosa et al , 2008;Reich et al , 2010) and the effect of disturb factors upon forest communities (Jardel et al, 2004;Calderón-Aguilera et al, 2012).…”

“…Los patrones espacio-temporales de la vegetación han sido interpretados como resultado de la influencia de dos tipos de gradientes: a) los factores ambientales limitantes: luz, temperatura, agua, nutrientes del suelo, entre otros; y b) el disturbio natural o antropogénico (Jardel et al , 2004). Por ejemplo, la variación de la topografía (gradiente altitudinal) y los diversos tipos de suelos son variables físicas consideradas en la hipótesis general, que contribuyen a explicar la diversidad de especies en escalas geográficas locales utilizadas en otros ecosistemas de montaña en México (Jardel et al , 2004;Cuevas et al , 2008;González-Espinosa et al, 2008;Reich et al, 2010).…”

Spatial statistics in ecological analysis: from indices to functions

“…Additionally, working with big data sets ecologists should be ready to work collectively in order to collect, preserve and share the data across projects and research groups (Hampton et al 2013). Good examples for such data can be found in Perry et al (2002), Watt et al (2009), Reich et al (2010 and Pongpattananurak et al (2012).…”