1
Grouping Forest Tree Species on the Sierra Madre
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Occidental, Mexico
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Untersuchungen zur Strukturierung der artenreichen Mischwälder in der Sierra Madre Occidental, Mexico
5 Lujan-Soto, J. E. *, Corral-Rivas, J.J. *, Aguirre-Calderón, O. A. ** and Gadow, K. v. *** 1
6 7 8 9 10 11 12 13
reference: Lujan-Soto, J.E.; Corral-Rivas, J.J., Aguirre-Calderon, O.A. and Gadow, K. v., 2015: Grouping Forest Tree Species on the Sierra Madre Occidental, Mexico. Allgemeine Forst und Jagdzeitung 186 (3-4): 63-71
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Abstract The Sierra Madre Occidental in the Mexican State of Durango, is home to about five million ha of species-rich forest ecosystems. Many people live in or near these forests and depend on them for their livelihood. The preservation of the species richness of this unique resource requires improved understanding of individual species functioning, effective modeling and advanced methods of monitoring. Identifying similarities and differences between individual tree species is a key to achieving these aims. Accordingly, the purpose of this study is to define species cohorts based on Durango's extensive network of permanent observational studies. We review different approaches of simplifying species-rich forest communities. A height-growth ordenation, that had been used in several previous studies, provided the motivation for a new method of vertical stratification, based on the differentiation between canopy and permanent subcanopy species. The first group was further subdivided into mature and immature individuals using the relationship between diameters and heights within a bivariate mixed normal distribution.
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Zusammenfassung In der Sierra Madre Occidental im Mexikanischen Staat Durango, gibt es ungefähr fünf Millionen ha artenreicher Waldökosysteme, die von dort ansässigen Gemeinschaften seit etwa 100 Jahren selektiv genutzt werden. Der Erhalt der Artenvielfalt dieser einmaligen Wälder erfordert ein besseres Verständnis einzelner Arten, effektive Modellierung der Walddynamik und verbessertes Monitoring der Nutzungen. Das Verständnis von Unterschieden und Ähnlichkeiten zwischen den Baumarten ist eine wichtige Voraussetzung zur Realisierung dieser Voraussetzungen. Dementsprechend verfolgt diese Untersuchung das Ziel, auf der Basis eines umfangreichen Datenfundus aus Dauerversuchsflächen, Arten-Kohorten zu definieren. Ähnliches hat man in zahlreichen Arbeiten, vor allem in Tropenwäldern versucht, bisher aber nur mit mässigem Erfolg. Nach einem Vergleich unterschiedlicher Methoden zur Strukturierung artenreicher Ökosysteme werden zwei Ansätze verfolgt, die etwas das gleiche Ergebnis erzielen: a) Gruppierung auf der Basis von Höhen-Zuwachs Relationen einzelner Arten und b) ein neuer Ansatz der vertikalen Stratifizierung. Neu ist insbesondere die Unterteilung der herrschenden Arten in bereits dominante und noch unterständige mit Hilfe der bivariaten Durchmesser-Höhen Mischverteilung.
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Keywords: Mexico; observational study; Pinus; Quercus; Durango; Classification 1*(Universidad Juárez del Estado de Durango, México); **(Universidad Autonóma de Nuevo León, México); *** (Georg-August Universität Göttingen, Germany; University of Stellenbosch, South Africa). Corresponding author:
[email protected]
2 1
Schlagworte: Mexiko; Dauerversuchsflächen;
2 3
1 Background
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The Sierra Madre Occidental in the Mexican State of Durango, is home to about five million ha
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of species-rich forests. A great variety of coniferous and broadleaved tree species, including 20
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pine and 43 oak species, are known to occur in Durango (GONZÁLEZ et al., 2007). More than
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85 percent of the forest area of Durango is owned by rural communities known as Ejidos and
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Comunidades who manage their land with some level of governmental control (CORRAL-RIVAS,
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2006). Many people live in or near these forested areas and depend on them for their livelihood.
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These natural resources are very important, not only for socio-economic reasons, but also for the
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continued existence of this unique resource.
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During the past century, the species-rich forests of the Sierra Madre Occidental have been
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subject to various forms of selective harvesting by the local communities. With some exceptions
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where areas have been clearfelled or managed in shelterwood systems with subsequent formation
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of pure even-aged commercial forests, the majority of the natural species communities have
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survived to this day (CORRAL-RIVAS et. al., 2012). The large scale existence of natural forest
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ecosystems on which local communities depend for their livelihood, presents a particular
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challenge for ecologists who study ecosystem response to human use. We believe that a basic
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element of such studies is the simplification of complex tree communities by grouping species.
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1.1 Why Simplify a Forest Community ?
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According to PICARD et al. (2010), the grouping of species contributes to improved
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understanding of the functioning of a species-rich ecosystem. Aggregating species into well-
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defined cohorts may also facilitate modeling of the dynamics of such ecosystems because a high
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species diversity is usually associated with the scarcity of data for certain rare species which
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prevents the development of models that require sufficient data for fitting.
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Grouping tree species has also important practical implications. Multi-species forest
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ecosystems are selectively harvested in different regions of the world (HAIGHT, 1987; AMMER
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et al., 2011; SCHÜTZ et al., 2012). Continuous cover forestry (CCF) has been practiced for more
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than a century in Europe. Less well-known is the equally long-term, and possibly more extensive,
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application of selective harvesting in the community forests of the Mexican Sierra Madre Occidental
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(PÉREZ-VERDÍN et al., 2009). This observation provides increasing motivation for evaluating
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the suitability of selective silviculture in multi-species ecosystems. Selective harvesting modifies
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growing spaces and spatial niches. Forest management influences tree size and species
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distributions and the spatial mingling of tree sizes and tree species, thus causing major changes in
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forest structure (GADOW et al., 2011). A particularly challenging objective of CCF silviculture is
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to derive economic benefits without modifying the key features (including the resource value) of
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the natural ecosystem. Accordingly, any meaningful simplification of a complex forest
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community may contribute significantly to improved silviculture and more effective control of
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harvesting activities.
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1.2 Methods of Grouping Tree Species
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Numerous tree species coexist in the forests of the Sierra Madre Occidental (GONZÁLEZ et al.,
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2007). Individual species have adapted to specific site conditions and they are capable of
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responding to a range of complex interactions to improve their chances of survival and
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reproduction. Some species produce valuable timber and some are fast growing. Some species are
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shade tolerant, others are light demanding. Most tree species, when mature, occupy specific
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vertical layers, some occur within particular habitats. Accordingly, aggregating species to form
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relatively homogeneous groups may be based on different methods and different criteria.
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Numerous studies have been published that deal with the relationship between habitat and
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species composition. Some species were distributed randomly, while others occurred in a
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clumped pattern at different scales (HUBBELL and FOSTER, 1983; DALLING et al., 2007;
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ZHANG et al., 2009a,b), or preferred specific soil attributes (PALMER, 1990). These studies
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show that species grouping by habitat is not very effective because, even in very large plots,
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topography has been found to determine the geographic range of a species less often than small-
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scale factors such as dispersion and gap recruitment (PLOTKIN et al., 2000). Furthermore, a
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species-rich environment may support a large number of species which may occur across
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different habitats, thus grouping by habitat is not very meaningful.
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Classifying species into groups is a way to understand the functioning of species-rich
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ecosystems, and specific statistical techniques have been used to do that. A plant functional
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attributes approach was presented by VANCLAY et al. (1997). PICARD et al. (2012) used
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multiple correspondence analysis to identify associations between different classifications to
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establish whether different techniques produce consistent classifications in a tropical rain forest
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in French Guiana. They found a consensus on the potential size of trees but no consensus was
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found for growth rate, nor wood density, traits that are correlated with light requirement.
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A popular method of species grouping is size-growth ordenation. The traits used by
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FAVRICHON (1994) for species grouping were mean diameter and diameter increments by size
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class. ALDER et al. (2002) presented a study involving an ordination of species' mean increment
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(Id) on the 95% percentile of the diameter distribution (D95), using a cluster analysis based on
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Euclidian distance between points. They compared growth rates and typical size for 204 tree
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species from permanent sample plots in Brazil, Costa Rica, Guayana and Papua New Guinea.
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Their clustering algorithm produced 16 centroids. Low growth rates associated with five of the
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16 groups were assumed to be indicators of shade tolerance, had higher wood densities and
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typically occurred in a lower canopy or understorey position. Other groups had higher increment,
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and were light demanding with lower density timber. ALDER et al. (2002) assigned names to
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specific group clusters, like "Pioneers", Emergents" or "Subcanopy Species" (see also ALDER
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and SILVA, 2000).
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The purely taxonomic approach of grouping species did not appear to show much
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promise. Even on the Sierra Madre Occidental, with two dominant genus' Pinus (16 species) and
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Quercus (27 species), species traits which may explain differences in ecological strategy, vary
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widely. CORNELISSEN and CORNWELL (2014) therefore, propose that plant species effects
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on ecosystem functions should be mapped onto the Tree of Life by analysing traits and
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phylogenies together, thus linking ecological and evolutionary information. Recent advances in
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assessing plant species for the traits that support these functions combined with genetic
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screening and bioinformatics could form the basis for developing molecular plant phylogenies
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that could be used to develop distinct species clusters for particular ecosystems. That advanced
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taxonomic approach of aggregating species shows promise for future studies.
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A fifth approach involves grouping based on vertical layers. For example, BOSSEL and
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KRIEGER (1994) classified species as understorey, "treelets", canopy trees, emergents and
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pioneers. In a two-stage approach KÖHLER et al. (2000) classified species first by potential
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height, the second entry was successional status. The "layer" approach appears sound. All species
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on the Sierra Madre Occidental, occupy one of two vertical strata as a mature individual: the
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canopy (span. dosel) and the subcanopy (span. subdosel) stratum. The problem to be solved is then
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to differentiate between immature (Bossel's "treelets") and mature canopy species to obtain three
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groups to which all species can be uniquely assigned.
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Finally, a species classification based on the utilisation potential is useful where
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ecosystems represent a commercially important resource. Trade categories associated with
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particular species are needed to ensure sustainable harvesting, and depletion of standing crop
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values, in species-rich ecosystems managed by selective silviculture.
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The objective of this study is to develop a systematic approach of grouping tree species
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for the community forests of the Sierra Madre Occidental, based on 426 permanent
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observational field plots with mapped trees and detailed measurements. We review the extensive
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literature on simplifying a species-rich ecosystem and present a new approach of species
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grouping. The study will thus contribute to our understanding of the functioning of that species-
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rich ecosystem, and at the same time provide a more sound basis for sustainable use.
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2 Material and Methods
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2.1 The Database
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This study is based on a network of permanent observational sites established between 2007 and
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2011, and with the first set of remeasurements taken in 2013 and 2014. All the plots are square
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covering an area of 50x50 m. They are distributed systematically (with some exceptions), with a
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variable grid ranging from 3 to 5 kilometers, depending on the size of the community forest areas
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(known as Ejidos in Mexico). Field assessments follow the methodologies developed by
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CORRAL-RIVAS et al. (2009; 2012). A variety of variables, including tag number, species code,
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breast height diameter (d, cm), total tree height (h, m), height to the live crown (m), azimuth (º)
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and radius (m) from the centre of the plot of all trees equal or larger than 7.5 cm in diameter are
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recorded. Currently, the database includes such measurements for 68 252 trees. Altogether 94 of
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the 426 plots were re-measured, and increment data are available for 14 397 trees in that set. The
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maximum and minimum values of the variables indicate that the observational network
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adequately represents the variability of the forests of the Sierra Madre Occidental.
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2.2 Size-Growth Ordenation
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FAVRICHON (1994) and ALDER et al. (2002) used species' mean dbh and dbh increment in
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their grouping study. The database of the Sierra Madre Occidental includes, in addition to
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diameters, also height measurements of all individual trees. Therefore, in this study, it is possible
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to evaluate an ordination using mean tree heights and mean dbh growth rates of those species
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that have been remeasured. All species for which at least 20 remeasurement of diameter and
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height are available, are included in the analysis.
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2.3 Vertical Stratification to Define Species Cohorts
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Some tree species reach the canopy when mature, while others have adapted to remain in the
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subcanopy layer even when mature. Some of these may form a shrub layer. While this grouping
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seems to be largely experienced-based, it is nevertheless a logical approach that has been followed
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by a number of authors (BOSSEL and KRIEGER, 1994; FAVRICHON, 1994; KÖHLER and
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HUTH, 1998; ALDER et al., 2002). To be objective, the grouping should be based on well-
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defined criteria and field measurements, tree heights being obviously more suitable than tree
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diameters.
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2.3.1
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In this study, we define canopy species as those who eventually occupy the upper stratum. To
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identify the members of this group, we are scanning all trees in the 426 study areas, following
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two approaches:
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a)
Canopy Species: Mature Individuals
Individuals that have reached at least the 95 percentile height (21.8 m) of the height
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distribution involving all trees in the database, are assigned to the group of "canopy
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species";
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b) All study areas which include at least one tree with a height 20 m or more are evaluated.
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All species that have reached a height of at least 80 percent of the maximum height in one
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of those study areas are assigned to the group of "canopy species".
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The problem is rather trivial and can be solved easily using the extensive database with its broad
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site coverage. Somewhat more challenging is the task of distinguishing between mature and
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immature canopy species, those that have already reached the canopy, and those that still exist
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beneath it.
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2.3.2
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The population of canopy species is composed of mature and immature individuals. The problem
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is to find an objective and plausible way of distinguishing between these two groups. Previous
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studies in species-rich natural forests in Europe and China have shown that the height-diameter
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ratios of the mature and immature canopy species are distinctly different (ZUCCHINI et al.,
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2001; ZHANG et al., 2014). These ratios tend to be lower in the trees that have already reached
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the canopy and higher in those individuals that still grow in the subcanopy stratum. The
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parameters of the bivariate mixture model used to distinguish between the two clusters, have
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familiar interpretations and may be estimated using the R-function mclust (FRALEY et al., 2012).
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Let f(d,h) denote the bivariate probability density function of diameter and height. The proposed
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model then is
Canopy Species: Immature Individuals
f (d , h) = α ⋅ n1 (d , h) + (1 − α ) ⋅ n2 (d , h) 27
where α, a parameter in the interval (0, 1), determines the proportion of trees belonging to each
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of the two component bivariate normal distributions n1(d,h) and n2(d,h). The parameters of nj(d,h)
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are the expectations udj, uhj; the variances σ2dj and σ2hj, and the correlation coefficients, ρj(j=1, 2).
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The diameter separating the two clusters may be used to differentiate between mature and
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immature individuals. That "segregating diameter" is species-specific and has to be identified
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using the available database.
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2.3.3
Subcanopy Species
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Beneath the canopy there is another layer of vegetation, called the subcanopy or understory. That
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layer is formed by low woody plants, sometimes with multiple stems from the base, that attain a
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height at maturity which is often considerably less than the canopy height. In the present study,
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we define subcanopy species as all those that are not identified as canopy species
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3 Results
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3.1 Size Growth Ordenation
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Figure 1 shows the mean dbh growth rates over mean tree heights of all species for which at least
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20 remeasurement are available in the remeasured data set of 14 397 trees. The following 33 tree species are included:
11 12 13 14 15 16 17 18 19
Abies durangensis (Abdur); Alnus firmifolia (Alfir); Alnus jorullensis (Aljor); Arbutus arizonica (Arbari); Arbutus bicolor (Arbbic); Arbutus madrensis (Arbmad); Arbutus tessellata (Arbtes); Arbutus xalapensis (Arbxal); Cupressus lusitanica (Clus); Juniperus deppeana (Jdep); Juniperus durangensis (Jdur); Picea chihuahuana (Picea); Pinus arizonica (Par); Pinus strobiformis (Pay); Pinus cooperi (Pcop); Pinus durangensis (Pdur); Pinus engelmannii (Pen); Pinus leiophylla (Plei); Pinus lumholtzii (Plum); Pinus teocote (Pteo); Pseudotsuga menziesii (PSmen); Quercus arizonica (Qari); Quercus conzattii (Qcon ); Quercus crassifolia (Qcras); Quercus durifolia (Qdur); Quercus eduardii (Qed); Quercus jonesii (Qjon); Quercus laeta (Qlac); Quercus mcvaughii (Qmcv); Quercus obtusata (Qob); Quercus radiata (Qrad); Quercus rugosa (Qrug); Quercus sideroxyla (Qsid).
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Fig. 1 (a) presents the height-growth ordenation of all 33 species. The abbreviated symbol which
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appears within brackets following the species name, is introduced to facilitate identification of
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individual species. Three shaded ellipses are used to present a first general impression of
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groupings.
0.4 0.3 0.1
0.2
meanid
0.5
0.6
10
5
10
15
20
meanht
(a) three clusters 24
(b) two vertical layers
Figure 1. Height-growth ordenation of 33 tree species for which at least 20 remeasurement are available.
8 Gruppierung nach Höhe und Zuwachs von 33 Baumarten, für die mindestens 20 Wiederholaufnahmen vorliegen.
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The shaded area in the lower left of Fig. 1(a) includes all 12 Quercus species, the longitudinal one
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above includes 5 species of Arbutus and two species of Alnus whereas the 8 species of Pinus are
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located in the area with mean heights between 10 and 15 m. Fig. 1 (b) shows the results of a
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cluster algorithm imposed on the data2. The graph presents a vertical structure with two distinct
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layers, a lower layer with a broad range of diameter growth rates (0.2-0.5 cm/year) and an upper
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layer with a more narrow range of dbh growth (0.28-0.4 cm/year). The lower layer includes
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various species of Arbutus, Alnus and Quercus with mean heights ranging from about 4-8 m. With
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similar average heights, the species of Arbutus and Alnus have on average higher mean growth
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rates than those of the genus Quercus. The most prominent group in the upper layer, with mean
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heights ranging from about 8-15 m are the 8 species of Pinus. Abies durangensis, Picea chihuahuana
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and Pseudotsuga menziesii have the biggest average heights. However, these species only occur in
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isolated populations and are not representative of the Sierra Madre Occidental (AGUIRRE et al.,
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2003).
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3.2 Vertical Stratification
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Eighteen, out of the total of 64 tree species that are recorded in the database, were classified
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according to method (a) described in section 2.3.1. Two of these species that had reached the 95
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percentile height (Cupressus lusitanica and Alnus jorullensis), were subsequently excluded because
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they had not been identified as being dominant within their local environment and did not meet
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the requirements of method (b). The resulting 16 canopy species, of which 10 are pine and 4 are
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oak species, are presented in Table 1 while Figure 2 shows two examples of the fitted mixture
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model.
30 10 0
0 0
20
40
60
80
dbh (cm)
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20
height (m)
20 10
height (m)
30
40
40
1
0
20
40
60
80
dbh (cm)
Figure 2. Two typical graphs of the fitted mixture model used to differentiate between mature and immature 2
using the R-function mclust (FRALEY et al., 2012)
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individuals of the canopy species Pinus arizonica (left) and Pseudotsuga menziesii (right). The lines represent the (0.05, 0.25, 0.5, 0.75, 0.95) quantiles of the bivariate density.
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Zwei beispielhafte Grafiken des angepassten Mischmodells zur Unterscheidung von voll entwickelten und unterständigen Individuen der herrschenden Baumart Pinus arizonica (links) und Pseudotsuga menziesii (rechts).
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The mixing probabilities in Table 1 represent the proportions of observations in the mature and
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immature clusters. The cluster means refer to the mean diameters of the immature and mature
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individuals. The segregating diameter is that dbh which separates mature and immature canopy
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species, based on the results of the bivariate mixture model. Its value is listed separately for each
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species in the last column of Table 1, rounded to the full centimetre.
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Table 1. The 16 canopy species that have met the requirements of condition (b) described in section 2.2.1. The column headings "immature" and "mature" refer to the immature and mature clusters respectively. The segregating dbh is that dbh which separates mature and immature canopy species, based on the results of the bivariate mixture model.
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Die 16 vorherrschenden Arten, die die Bedingung (b) im Abschnitt 2.2.1 erfüllt haben. Die Spaltenüberschriften "immature" und "mature" beziehen sich auf die herrschenden bzw. unterständigen Cluster. Species Pinus arizonica Pinus strobiformis Pinus chihuahuana Pinus cooperi Pinus durangensis Pinus engelmannii Pinus herrerae Pinus leiophylla Pinus lumholtzii Pinus teocote Pseudotsuga menziesii Picea chihuahuana Quercus crassifolia Quercus sideroxyla Quercus durifolia Quercus rugosa
Mixing probabilities immature mature 0.51 0.49 0.55 0.45 0.24 0.76 0.56 0.44 0.50 0.50 0.16 0.84 0.28 0.72 0.50 0.50 0.55 0.45 0.37 0.63 0.59 0.41 0.70 0.30 0.18 0.82 0.30 0.70 0.44 0.56 0.38 0.62
Means DH.DBH immature mature 11.7 27.8 12.1 23.2 14.9 15.8 13.4 28.4 11.6 25.2 16.7 38.6 24.8 39.6 13.4 27.2 10.9 18.8 26.2 12.7 14.8 39.5 12.0 53.7 15.1 37.2 13.2 30.3 10.3 18.8 12.7 29.8
segregating dbh (cm) 20 18 15 21 18 28 32 20 15 19 27 33 26 22 15 21
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Picea chihuahuana is a rare species which occurs in isolated small populations, and the available
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observations are insufficient to separate the two clusters. We nevertheless provide an initial
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estimate based on the available observations.
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3.3 Permanent Subcanopy Species
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The following permanent subcanopy species were found to occur in the 429 observational study
22
areas:
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Alnus firmifolia; Arbutus arizonica; Arbutus bicolor; Arbutus bicolor; Arbutus madrensis; Arbutus tessellata; Arbutus xalapensis; Crataegus spp; Cupressus arizonica; Fraxinus trifoliata; Guazuma ulmifolia; Juniperus deppeana; Juniperus durangensis; Juniperus flaccida; Juniperus monosperma; Pinus cembroides; Pinus
10 1 2 3 4 5
devoniana; Pinus douglasiana; Pinus herrerae; Pinus michoacana; Pinus tenuifolia; Populus tremuloides; Prunus serotina; Quercus arizonica; Quercus candicans; Quercus coccolobifolia; Quercus conzattii; Quercus depressipes; Quercus eduardii; Quercus emoryi; Quercus fulva; Quercus gentryi; Quercus hypoleucoides; Quercus jonesii; Quercus laeta; Quercus grisea; Quercus laurina; Quercus mcvaughii; Quercus obtusata; Quercus radiata; Quercus resinosa; Quercus rugosa; Quercus tarahumara; Quercus urbanii; Quercus viminea.
6
The majority of these species is economically unimportant. Interestingly, the database shows that
7
the selective management practiced on the Sierra Madre Occidental has not caused a major
8
change in the natural species richness.
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3.4 Cohort-Specification
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Based on the field measurements and the methods described in section 2, all individuals which
11
belong to a particular vertical stratum are assigned to one of three cohorts. Roman numerals are
12
used to identify the cohorts: mature canopy trees (I); immature canopy trees (II); permanent
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subcanopy trees of (III). Figure 3 presents a schematic view of a hypothetical multi-species forest
14
with individual trees assigned to the three cohorts.
I
I
III II
II III II III
III
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Figure 3. Schematic representation of different species cohorts in a multi-species forest, to illustrate the approach, based on the method of classification described in section 2.
18 19
Schematische Darstellung der unterschiedlichen Artengruppen in einem artenreichen Mischwald. Der methodische Ansatz wird im Abschnitt 2 erläutert.
20
It is possible of course, and perhaps also desirable, to develop more intricate classifications
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involving additional criteria, but the purpose of this study was to develop a robust initial
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grouping with few cohorts based on an objective statistical approach.
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4 Discussion
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4.1 Simplification of Species Rich Ecosystems
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The structure and functioning of species-rich ecosystems, like the forests of the Sierra Madre
11 1
Occidental, may be better understood when traits are identified that are shared by certain
2
species. A high species diversity is usually associated with the scarcity of data for certain rare
3
species which prevents the development of models that require sufficient data for fitting. Thus,
4
aggregating tree species into well-defined cohorts may facilitate modeling of the dynamics of such
5
ecosystems. Grouping tree species has also important practical implications. Multi-species forest
6
ecosystems are selectively harvested in different regions of the world. Preserving the species
7
richness in such ecosystems is often facilitated when species are grouped.
8
4.2 The Ecological Importance of Human Activity
9
Recent surveys have shown that virtually all ecosystems of the earth are utilized by man, - where
10
"utilization" includes the few remaining areas managed for conservation (KAREIVA et al., 2007).
11
The distinction between between Nature and Culture has become blurred, and a projection of
12
current trends leads to the conclusion that the earth will be even more affected by man in the
13
future. All ecosystems are used by humans and there is increasing concern about the relevance of
14
traditional research dealing with purely "natural" systems (LEINFELDER et al., 2012).
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Accordingly, CRUTZEN (2002) proposed to rename the current geological period Holocene and
16
to call it Anthropocene3, in which humans are an integral part of nature, constantly modifying the
17
planet. Man is a part of nature, and according to LEOPOLD (1949)4 human activities are
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fortunately not only destructive, but also increasingly constructive and creative in designing and
19
utilizing viable ecosystems, like the unique forests in the Mexican Sierra Madre Occidental.
20
Certification to internationally accepted standards of tree harvesting is increasingly seen as a
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necessary requirement in the forestry sector. Preserving species richness has become one of the
22
guiding principles ecosystem management.
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4.3 The Link between Plant Traits and Function
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Multi-species forests tend to be composed of different tree species which thrive in particular
25
habitats and respond to contrasting environmental conditions by genotypic and phenotypic
26
variation (ADAMS, 1993). The habitat is defined by certain site conditions and by specific spatial
27
constellations and physical relationships within the immediate neighborhood. Some species
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eventually reach the canopy as dominant individuals while others never do. Such simplifications
29
can be very useful because studies that have shown how plant traits affect the performance of
30
individual species, still lack the ability to predict community assembly from these relationships
31
(LEBRIJA-TREJOS et al., 2010). 3
see Zalasiewicz et al. (2008) "That man is, in fact, only a member of a biotic team is shown by an ecological interpretation of history".
4
12 1
This is partly due to the fact that the link between plant traits and function to the
2
environment is mostly conceptualized and few studies directly correlate them (McGILL et al.
3
2006, VILE et al. 2006). The importance of traits differs with ecosystem conditions and the scale
4
of study, which adds another dimension (WRIGHT et al. 2005, ACKERLY and CORNWELL
5
2007). LEBRIJA-TREJOS et al. (2010) predict community assembly by air temperature while
6
maximum height, important for a species’ competitive performance in many systems (THOMAS
7
1996, WESTOBY et al. 2002, POORTER et al. 2006), was irrelevant for succession in their
8
tropical dry forest community.
9
4.4 Additional Criteria
10
Additional research is required if this first level grouping needs to be extended to include
11
additional criteria, such as growth rates including site and competition effects. Another important
12
dimension is the timber value of the different species. A large part of the ecosystems of the Sierra
13
Madre Occidental are being utilized for timber production and the economic potential of the
14
different tree species is an important management criterion. The optimum dbh of group I pine
15
and oak trees selected for harvest is 35 and 40 cm, respectively while the prices per m3 fluctuate
16
between 80$ and 100$ for pine timber and 60$ and 80$ for oak timber. Further research is
17
required to substantiate this information.
18
4.5 Preserving Species Richness in the Anthropocene
19
Some of these studies are lacking extensive networks of permanent observational field plots with
20
mapped trees, a broad geographical coverage and repeated measurements, like the Durango
21
Forest Observational Network on which this analysis is based. Accordingly, it has been quite
22
straightforward to classify certain species as canopy species and others as subcanopy species by
23
scanning the database and evaluating the measured tree heights in the vicinity of the maximum
24
height in each plot. Finding a suitable distinction between mature and immature canopy trees has
25
been somewhat more challenging. Eventually, the use of a bivariate normal mixture turned to be
26
suitable for estimating a dbh, separately for each species, that segregates the mature and
27
immature canopy cohorts. The basic grouping presented in this study may be extended to include
28
specific traits (tree growth related to site and competition), trait combinations (tree height &
29
Growth) or economic criteria (timber value).
30
Multi-species forests are often believed to be superior to plantation monocultures in
31
addressing a wide range of expectations. For these reasons, there is wide and increasing interest
32
in developing management methods to rehabilitate disturbed natural forests, to re-convert
33
plantation monocultures to natural ecosystems or to manage existing multi-species forests, like
13 1
those on the Sierra Madre Occidental, more effectively. The preservation of species richness in
2
such complex ecosystems requires advanced methods of management. Accordingly, Gadow et al.
3
(2013) presented different retention strategies for selectively managed natural forests. They found
4
that especially relevant are those that do not prescribe the harvest amount but rather the residual
5
forest structure, in terms of tree species in combination with tree dimensions, remaining after the
6
harvest. Specific details need to be adapted for different ecosystems, and one of the basic
7
requirements is the grouping of species into cohorts with similar traits.
8
5 Acknowledgements
9
This research was supported by Fondo Sectorial CONAFOR-CONACYT (grant: CONAFOR-
10
CONACYT 115900), and SEP-PROMEP (grant: Seguimiento y Evaluación de Sitios
11
Permanentes de Investigación Forestal y el Impacto Socioeconómico del Manejo Forestal en el
12
Norte de México).
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
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