Study On Functional Groups Of The Natural Tropical Forests At Landscape Scale In Hainan Island, South China

As one of the most species-diverse ecosystems in China, tropical forests of Hainan Island are increasingly affected by various disturbances. Due to extensive deforestation and unreasonable land use, the area of primary forests has been reduced and that of secondary forest has been increased markedly. Landscape fragmentation and the change of spatial configuration of patches have dramatic impacts on coexistencence, growth, maintenance, and distribution of plant functional groups in forest landscapes. The past ecological researchs conducted in tropical forest of Hainan Island were mainly at community or below community levels, ecological researches at the landscape-level and even research based on functional group approach were, however, rarely explored. A typical tropical forest landscape-Bawangling region of Hainan Island was selected for this study. By combining methods of GPS positioning, field investigation of systematic grid sampling plots, remote sensing imagery interpretation, GIS analyses and model, functional groups of natural tropical forests at landscape scale were studied. The main contents and results of the research are as follows:(1) Grid squares were used as the sampling units for patch type and landscape pattern change analyses. To explore the impact of proximity of late-successional forest on secondary succession, the buffer analysis was performed. The results showed that between 1986 and 1998, except late-successional forest, the proportion of all other patch types changed significantly. However, between 1998 and 2002 most of patch types didn't changed significantly. Landscape metrics changed from 1986 to 2002. The number of patches and edge density increased, in contrast, mean patch size and core area decreased in most of patch types which indicated that fragmentation of the study region increased from 1986 to 2002. The distribution of secondary forest was significantly associated with the location of late-successional forest. The propotion of secondary forest with long recovery time was higher near the late-successional forest. However, with the increase of distance away from late-successional forest, the propotion of forest with short recovery time increased gradually.(2) To understand the spatiotemporal patterns and dynamics of species richness and abundance of functional groups in the tropical forest landscape, we classified the study area into eight landscape types based on vegetation type, disturbance manner and the time of recovery. The plant species were also aggregated into seven functional groups based on the growth form, successional status and plant size. The results showed that all functional groups, except for the emergent and canopy tree species which absent in early-successional clear cut lowland rain forest landscape, were present in all landscape types. Each landscape type had different numbers of dominant functional groups. There were similar species richness and abundance structure among functional groups between mid-successional clear cut lowland rain forest and old growth tropical coniferous forest. This similarity existed in selective logged and old-growth lowland rainforest, as well as among landscape types of montane rainforest. The functional groups with same successional status had similar patterns of species richness and abundance ratios among different landscape types. The variation patterns of functional groups along the successional stages in terms of species richness and abundance among the tropical lowland rainforest landscape types were more similar to each other than those in the tropical montane rainforest landscape types. This study provides further support for competition-colonization tradeoff and successional niche theory as opposed to models of neutrality and ecological equivalence.(3) To explore the relative influence of ecological factors on the distribution of plant functional groups in the tropical natural forest landscape, we derived functional group classification using the characteristics of species-specific wood density and potential maximum height. The three functional groups data sets were used, i.e. the presence-absence, the species richness, and abundance. Their relations to environmental, anthropogenetic disturbance, and spatial factors were analyzed with redundancy analysis (RDA). We then used a partial RDA with variation partitioning to specify which proportion of the variation in functional groups distribution pattern is explained by each of the three factors exclusively and which proportions are attributable to interactions between the factors. We found that purely environmental, purely anthropogenetic disturbance and the interaction between environmental and anthropogenetic disturbance were of comparable importance for controlling the changes of functional groups distribution. However, purely spatial influence and the interaction between spatial and other factors were relatively less important. Furthermore, we found that anthropogenetic disturbance types, topographical factors, soil types, gravel content, soil depth and X, Y coordinates all had unique impacts on functional groups distribution. Generally, hardwood functional groups were of high presence at the well-conditions, off-disturbance sites. But for softwood, the situation was the opposite. The distributional range of moderate density wood was wider than the other two groups. Except softwood shrub, the others having high species richness and abundance occurred at the well-conditioned sites, especially where soil depth was deeper.(4) A functional groups based approach was tried in this paper to classify landscape types in the species rich forest ecosystems. In this process, the TM imagery was treated by 5×5 low-pass mean filter, and the identification was made by the supervised maximum likelihood classification technique combining with field investigation data. The accuracy of classification was assessed by the classification error matrix and Kappa statistics. Finally, the distribution patterns of patches composed of different dominant functional groups and their relationships with successional stage and topography were analyzed by GIS. The results showed that seven land-use classes, four successional stage classes and six predominant functional group classes were classified from Landsat TM data with a reasonable degree (an average of up to 78 percent of overall classification accuracy) of class separability. Patches dominated by the pioneer were mainly distributed in the shrub lands, early recovery stage stand, flat area, sunny slopes and elevation ranges of＜850 m. While the patches dominated by the climax species had more occupancies in the middle and late recovery stages, shaded slopes, and elevation ranges of＞850 m. The slope gradient had no significant influences on the distribution patterns of patch types dominated by different functional groups.(5) To evaluate relationships between biomass and vegetation indices and to determine the spatial distribution of biomass for functional groups of different successional status in the tropical forest, we used the modelling procedure based on vegetation index and 135 sampling plots distributed over the study area. Biomass of different functional groups was obtained by regression analysis. Four vegetation indices (normalized difference vegetation index (NDV1), moisture vegetation index using Landsat's 5 (MVI5), moisture vegetation index using Landsat's band 7 (MVI7) and ratio vegetation index (RVI)) were correlated with measurements of aboveground biomass (climax and pioneer species biomass). Models describing the relationships between biomass and vegetation indices using stepwise linear regression analysis were also developed. Three biomass components maps were produced using the developed models. Residual maps were used to test the validity of the models. The results showed that MVI7 and MVI5 were the most important vegetation indices that are effective determinants of biomass for the climax species functional group, whereas NDVI and RVI seem to be good indices of biomass for pioneer species functional group. The strongly predictive percent areas for climax and pioneer species biomass model were 73.98 and 88.08 respectively. Simulated biomass of climax species was mainly continually distributed in the center, north and southwest parts of study area. However, simulated biomass of pioneer species was scattered over the study area.(6) To identify and characterize the relationships between landscape patterns of different habitat types and species richness of functional groups in the natural tropical forest region, we obtained patch types from a Thematic Mapper imagery classification using a supervised maximum likelihood classification in five adjacent sub-regions in the study region. The mean number of species of different functional groups was calculated for each patch type in each of the five sub-regions. The relationships between landscape patterns and the mean number of species of different functional groups were evaluated with multiple linear regression and principle component analysis. The results showed that significant relations existed between four landscape metrics and the mean number of species of different functional groups at the patch type level. The four landscape metrics included percentage of landscape (PLAND), edge density (PD), area-weighted mean shape index (SHAPE_AM) and total edge contrast index (TECI). The nature of these relations changed according to the functional groups considered, indicating that different functional groups reacted to the landscape pattern differently. The influences of landscape fragmentation appeared to be stronger for climax than for pioneer functional groups. With the increase of habitat diversity and more irregular shape of patch types, the species richness of climax functional groups increased more than that of the pioneer functional groups. While with the increase of PD and TECI, the species richness of pioneer functional groups didn't change significantly, on the contrary, the species richness of the climax functional groups decreased significantly.(7) Keystone species were identified within the context of functional groups using Dominance Index (DI). The GARP was used to estimate the keystone species' potential distribution and then the Receiver Operating Characteristics was used to evaluate the predictive performance. On this basis, by applying multiple linear regression analysis, we identified themajor factors determining the potential distributions of keystone species. The results showed that identification of keystone species within the pioneer, climax shrub and emergent tree functional groups was easier than within the climax subcanopy and climax canopy tree functional groups. Among the eight keystone species, pioneer species such as Melastoma sanguiueum, Aporosa chinensis and Liquidambar formosana (but except Adinandra hainanensis) had high occurrence probabilities in the north, west and southwest areas of study area. However, climax species such as Psychotria rubra, Ardisia quinquegona and Castanopsis hainanensis (but except Pinus merkusii) have high occurrence probabilities in the central, southeast and south regions of the study region. Minimum and maximum temperature, mean annual temperature and precipitation, aspect and altitude were the key factors determining the potential distributions of keystone species. Evaluation of model's performance indicated excellent predictive abilities of the model for predicting distributions of eight keystone species.(8) To reconstruct the functional groups based potential natural vegetation (PNV) in the tropical forest landscape, a series of procedures were conducted in this study. Firstly, we used the GIS-based GARP model to estimate the potential distributions of the eight functional groups. Secondly, we identified functional groups based realized natural vegetation (RNV) by using a supervised maximum likelihood classification technique. Thirdly, through overlaying occurrence probability surfaces of the functional groups while considering the distribution of RNV and interactions among the different functional groups, we derived the final functional groups based PNV. The results showed that the well model predictive performances for the functional groups were obtained, with all area under curve (AUC) up to 0.81. The distribution patterns of pioneer and climax functional groups were significantly different which reflected the differences of autoecological functional traits and reactions to environmental changes between the two functional group categories. The differences in spatial distribution patterns and in dispersal extents among functional groups based vegetation types indicated that the spatial autocorrelation existed in compositions of the different vegetation types.