Landscape-scale effects of geomorphological heterogeneity on variability of oak forest structure and composition in a monogenetic volcanic field

<p><i><b>Background</b></i>: Eruptive events in monogenetic volcanic fields create mosaics of lava fields that result in different successional times and stages, and environmental conditions. Such geomorphological heterogeneity may be related to patterns of vegetation structure, diversity, and composition across a landscape.</p> <p><i><b>Aims</b></i>: To examine landscape-scale effects of geomorphological heterogeneity on the spatial variability of oak forest structure and composition in a monogenetic volcanic field.</p> <p><i><b>Methods</b></i>: We sampled oak forests in six geomorphological units within a monogenetic volcanic field in El Tepozteco National Park and carried out redundancy analyses to relate environmental variables to forest composition and structure.</p> <p><i><b>Results</b></i>: Geomorphological heterogeneity explained 28.8 and 25.7% of the variation in canopy structure and composition, respectively, and 21.6% of the variation in understorey composition. Exposed rock was the most important predictor of understorey composition and canopy structure, while elevation was a better predictor of canopy composition. The role of chronic human disturbance as a driver of forest composition and structure was minor compared to that of geomorphological heterogeneity.</p> <p><i><b>Conclusions</b></i>: The geomorphological heterogeneity created by volcanic activity in this landscape drives oak forest variability; however, considerable unexplained variation in vegetation traits remains, which could be associated with unmeasured dimensions of environmental heterogeneity, neutral processes, and the effects of long-term human activity in the area.</p

Salas-Morales et al DataBase

The data contained in this data base are from 15 localities in Coastal Oaxaca, Mexico. The format is an Excel Book. Page 'Location' includes names and geographical coordinates of the localities. Page 'Height' contains the heights in m for all trees recorded in 10 2 x 50-m transects at each locality; next to the height of each individual the growth form of the plant is provided; the number of individuals varies between localities. The third page ('Environment') includes results of soil analyses for soil samples from each location; these analyses may come from the upper or the lower soil horizon; additionally, climatic data for each location are included, which were obtained through linear interpolation of data recorded at meteorological stations in the region with long climatic records

Appendix A. Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.

Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits

Predicting old-growth tropical forest attributes from very high resolution (VHR)-derived surface metrics

<p>Old-growth tropical forests are increasingly vanishing worldwide. Although the accurate quantification of tropical old-growth forests attributes is essential to understand, manage, and conserve their high diversity and biomass, conducting this task over large areas and at fine detail is not only expensive and time consuming, but also often practically impossible. This calls for the search for more efficient alternatives, particularly those based on remote sensing. In this study, we evaluate the potential of several surface metrics (tone and texture) extracted from very high resolution (VHR) satellite imagery to model the structural and diversity attributes of a tropical dry forest (TDF) in southern Mexico. We constructed simple linear models that used each forest attribute as dependent variables, and the tone and texture metrics extracted from several bands, the panchromatic (resolution = 0.5 m), red (R), infrared, and two vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI); resolution = 2 m), of a VHR image (GeoEye-1) as predictive variables. The significance of the models including one, two, two and its interaction, and three image metrics was evaluated by comparing them with null models. The structural characteristics of the TDF (basal area (BA), mean height, stem density) showed the highest modelling potential, with the goodness-of-fit (<i>R</i>²) values ranging from 0.58 to 0.66. Conversely, no significant models were obtained for total crown area (TCA) and all diversity attributes. Our results show that remote-sensing metrics detect the spatial variation in the structural attributes of this old-growth TDF better than they detect the variation in its diversity. Our ability to model forest attributes at large scales at fine detail (sampling plots <0.2 ha) can be much improved by combining the use of VHR imagery with an array as wide as possible of the image surface metrics, including both tone and texture.</p

Changes in the dominant plant strategies with succession.

<p>Stand basal area was used to indicate succession; it increased asymptotically with successional age and reflects successional change in vegetation structure. Functional composition was calculated using the community-weighted mean of species scores on the principal component axes. (a) Dry forest succession (open symbols, broken regression line) was characterized by changes along the first PCA axis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123741#pone.0123741.g001" target="_blank">Fig 1a</a>) and reflected changes from deciduous species to evergreen species that invest in a secure reproductive strategy. (b) Wet forest succession (filled symbols, continuous regression line) was characterized by changes along the second PCA axis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123741#pone.0123741.g001" target="_blank">Fig 1b</a>) and reflected changes from an acquisitive strategy to a conservative strategy. Given is the r², * P < 0.05; ** P < 0.01. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123741#pone.0123741.s002" target="_blank">S1 Fig</a> for the trends with fallow age as an indicator of succession.</p

Eigenvector scores of functional traits on the two main principal components for dry forest and for wet forest.

<p>Values in parentheses indicate variance accounted for by each axis.</p><p>^§Variable was ln-transformed.</p><p>Eigenvector scores of functional traits on the two main principal components for dry forest and for wet forest.</p

Correlation coefficients (CC) of all pairwise trait combinations (11 traits, resulting in 55 pairwise trait combinations per forest type, see Table 2) of dry forest species plotted against those of wet forest species.

<p>Correlation coefficients represent Spearman coefficients except when relating binary variables, then the Phi coefficient was used. The pairwise correlation coefficients of dry forest proved to be significantly correlated with those of the wet forest (Pearson product moment correlation [R], P < 0.001), indicating that trait spectra are consistent across the two different forest types.</p

Spearman coefficients of the pairwise relations between variables and the principal components (Fig 1).

<p>Relations between the binary variables (LC, De and Di) are Phi coefficients.</p><p>^§Variable was ln-transformed. Lower-left half of the matrix corresponds to dry forest species (n = 51), Upper-right half corresponds to wet forest species (n = 81).</p><p>* P < 0.05,</p><p>** P < 0.01,</p><p>*** P < 0.001.</p><p>Spearman coefficients of the pairwise relations between variables and the principal components (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123741#pone.0123741.g001" target="_blank">Fig 1</a>).</p

Gallardo-Cruz et al DataBase

The data contained in this data base are from 250 plots (10 × 10 m) distributed across a tropical landscape in southern Mexico. The format is an Excel Book. Page 'Species richness' includes UTM (zone 15) coordinates of the sampling plots and six species-richness sets: all species, legume species, legume trees, legume shrubs, legume forbs and legume climbers. Page ‘Surface metrics’ contains the values for 24 surface metrics derived from a Quickbird satellite image. Page ‘Moving window metrics’ contains the values for 20 moving window metrics derived from a classified Quickbird image. Page ‘Metadata’ contains the full description for each variable

Number of plant species, genera and families in 26 forest patches (FP) and 4 reference sites within a continuous forest (CF) sampled in the fragmented Lacandon rainforest, Chiapas, Mexico.

<p>The observed values (Obs) and the expected (Exp) after a coverage-based rarefaction (interpolation) are indicated for each site. The coverage estimator suggested by Chao and Jost <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098931#pone.0098931-Chao1" target="_blank">[27]</a> is also indicated for each case (<i>Ĉ_n</i>, see Methods).</p