Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico

Highlights

• Forest dynamics is complex, which is a synthetic result of climate, soil and their spatial heterogeneity properties.

• The explanatory power of soil on forest dynamics complex is modeled and compared with typical forest-climate models.

• Spatial heterogeneity of climate, soil, and forest types further enhance the complicated forest dynamics.

• Forest dynamics is comprehensively analyzed using different forest types, macroclimate factors, and soil models.

Abstract

Forest dynamics is complex, and the complexity could be a synthetic result of climate change. Specifically studying 11 forest type groups of the Gulf of Mexico coast region defined, we intended to explore and model the direct and indirect impacts of climate change on underlying forest dynamics. This study utilized normalized difference of vegetation index (NDVI) as a measurement indicator of forest dynamics, referring to the dynamics of canopy structure and phenology of forests, and for a given type of forests, seasonal and yearly NDVI values were applied to the quantification of its growth across the Gulf Coast. By utilizing geographically weighted regression (GWR) method, we related normalized difference vegetation index (NDVI) to precipitation, temperature, and silt and clay fractions in the soil. This study demonstrated an explanatory power of soil, besides the common macroclimate factors of precipitation, temperature, on explaining forest dynamics, which also revealed that the presence of spatiotemporal heterogeneity would affect model performance. Our results indicated that the model performance varied by forest type groups and seasons. The meteorology-soil model presented the best overall fit performance for White/Red/Jack Pine forests concerning R² (0.952), adjusted R² (0.905), Akaike information criterion (AIC, -1100) and residual sum of squares (RSS, 0.053) values. The comparative analysis of model performance also indicated that the meteorology-soil model has the best fit of data in summer. This study advanced the understanding of forests dynamics under conditions of climate change by highlighting the significance of soil, which is a significant confounding variable influencing forest activities but is often missed in forest-climate dynamics analysis.

Introduction

Climate has long been identified as the main factor that impacts on forest activities (Forman, 1964, Box, 1981, McKenney et al., 2007, Gómez-Mendoza and Arriaga, 2007). Climate is the dominant driver of spatial variation in forest growth (Toledo et al., 2011). Gómez‐Mendoza and Arriaga (2007) indicated that the long-term vegetation changes in the temperate forests of Mexico were deemed as a consequence of climate change. Climate change also triggers phenology change such as spring leaf unfolding and radial growth through fluctuations in precipitation and temperature. Hilker et al (2014) have found that the vegetation canopy of the Amazon rainforest was highly sensitive to fluctuation of precipitation. Additionally, Wang et al. (2011) have observed that vegetation growth in mid to high latitudes of North America is very sensitive to temperature change. Moreover, Li and Meng (2016) indicated that coastal forest dynamics could be explained by a combined effect of meteorological factors such as precipitation and temperature. In general, relationships between forest dynamics and meteorological factors are complex. There are several mechanisms by which meteorological factors could influence forest dynamics and many attempts have been made to explain forest dynamics by observations of climate change. The finding obtained by Zhao et al. (2010) indicated an equivalent role of precipitation and temperature in explaining vegetation dynamics in semiarid region in North China. Within some vegetation systems, precipitation was identified as the dominant factor affecting forest dynamics, and the temperature may play a minor role. For instance, Hilker et al. (2014) found that variations in precipitation accounted for more than half of the observed changes in NDVIs of Amazonian forests. Zhao et al. (2015) have revealed that precipitation has more significant influence than temperature on vegetation dynamics in semi-arid and sub-humid areas. Chuai et al. (2013) also demonstrated that the effect of precipitation on NDVI is more obvious as that of temperature for growing season vegetation in Inner Mongolia with arid and semi-arid climate. However, in other studies, temperature was considered as the main driving factor related to vegetation activities. For instance, Hao et al. (2012) proved that temperature is principally correlated with forest NDVI changes in cold and dry areas such as Qinghai – Tibet Plateau. Pravalie et al. (2014) also noted that temperature is the main climate parameter to have an impact on the forest vegetation at Southwestern Romania. A continental-scale study conducted by Piao et al. (2014) indicated that the temperature is mostly correlated with variation of growing season NDVI in the northern temperate and the arctic region.

Various soil properties are related to forest vegetation characteristics (Levine et al., 1994). It was believed that the variation in the relationship between vegetation and precipitation could be disturbed due to the influence of soil backgrounds, such as soil type (Chen et al., 2014) and soil permeability (Kang et al., 2014). Seasonal variations in optical properties of the soil were observed related to temporal changes of NDVI (Soudani et al., 2012). Forests depend on the availability of water and nutrients as essential resources for growth (Toledo et al., 2011). Water and nutrient availabilities are likely to promote the formation of forest and tree growth (Murphy and Bowman, 2012). The soil texture plays an essential role in the forest’s ability to extract water and nutrients. Soil texture strongly influences many hydrologic and biogeochemical processes in forest ecosystems through its effects on belowground carbon storage, water availability and nutrient retention (Epstein et al., 1997, Schoenholtz et al., 2000, Silver et al., 2000, Michelot et al., 2012). The role of soil texture to soil quality is retention and transport of nutrients and water (Doran and Parkin, 1994, Schoenholtz et al., 2000). Soil texture influences soil water flow, availability, storage and soil moisture (Pachepsky and Rawls, 2003, Bronick and Lal, 2005, Prepas et al., 2006, Kreutzweiser et al., 2008). As a basic soil quality indicator, soil texture is used for comparing soil quality and is a dominant soil property that influences most other properties and processes of soil (Schoenholtz et al., 2000). Generally, soil texture was believed to be a fundamental qualitative property that could be used in forest dynamics modeling.

Forest dynamics has been studied and interpreted from various perspectives. Forest dynamics sometime refers to the forest deforestation and degradation (Giri et al., 2007) or the forest gap formation and closure (Bossel and Krieger, 1991, Yamamoto, 2000). Generally, the study of forest dynamics is focused on changes in forest structure and composition arising from natural or anthropogenic forces (Pretzsch, 2009). NDVI was demonstrated well-correlated with biophysical parameters (e.g. vegetation biomass and the fraction of green vegetation cover) and photosynthetic forest activities (Myneni et al., 1995, Birky, 2001, Boelman et al., 2003, Meng et al., 2007, Verbesselt et al., 2010, de Jong et al., 2011, Leon et al., 2012, Li and Meng, 2016). Seasonal variations of NDVI were found related to vegetation phenology (McCloy and Lucht, 2004) and were used as the proxy for 'forest dynamics' (Beck et al., 2006, Li and Meng, 2016). Forest NDVI could vary among different species (Gamon et al., 2015) and seasons (Soudani et al., 2012). Deciduous forest NDVI could be highly correlated with morphological changes such as green biomass and evergreen forest canopy structure is relatively stable over the year (Gamon et al., 2015). Soudani et al. (2012) have found that NDVI is undergoing a decrease during winter and reaches its minimum value prior to spring, which is consistent with the phenology. The forest-cover extent, forest distribution and decreases or increases in green biomass can be also associated with forest harvest or regeneration (Wilson and Sader, 2002, Drummond and Loveland, 2010). Forest dynamics also could be interrupted by human activities. For instance, forest planting and timber harvesting altered the forest species composition, structure, ecosystem processes, and landscape patterns (Bossel and Krieger, 1991, Bu et al., 2008, Thompson et al., 2011, Chuai et al., 2013).

Forests differ in their tolerance of and requirements from the environment so that their associations with underlying factors such as meteorological factors and soil might vary as a function of environmental conditions (Swaine, 1996). For instance, changes in the relationship between vegetation and climatic variables (e.g. precipitation and temperature) could be caused by spatial variations in surface properties such as vegetation type, soil type and land use (Usman et al., 2013). Previous studies have explored relationships between climatic variabilities and forest growth within different forest types. According to the results obtained by Michelot et al. (2012), different forest types such as beech, pine and oak differed in their correlations with meteorological and soil conditions. For instance, the evergreen needle forests such as pine forests with deep rooting systems were found less sensitive to climatic conditions than others (Piao et al., 2004, Immerzeel et al., 2009). Omuto et al. (2010) demonstrated that the vegetation types did not have a uniform response to rainfall in drylands. Additionally, Karnieli et al. 2010 have found that NDVI-temperature relationships varied with vegetation types. Moreover, lag‐time effects of previous season's temperature and precipitation also varied among vegetation types (Chuai et al., 2013). Mather and Yoshioka (1968) believed that climate impacts vegetation directly through climatic factors such as precipitation and temperature, and indirectly through the effects that climatic factors have on soil conditions.

Although the forests-meteorology and forests-soil relationships have been well analyzed as summarized as above, the combined effects of precipitation, temperature and soil are much less explored. Especially, considering the strong spatial heterogeneity of meteorological and soil conditions in the Gulf Coast of Mexico, we hypothesized that changes in meteorological and soil factors could significantly explain the Gulf Coast forest dynamics. NDVI was used as an indicator of forest dynamics. We applied the geographically weighted regression (GWR) method incorporating multivariate into spatial modeling to quantify spatial relationships, as it considers the spatial heterogeneity among data observations into parameter estimation (Zhang et al., 2009). In order to explore the spatial dependence of forest dynamics on precipitation, temperature and soil texture, we attempted to fit spatial regression models to the forest-precipitation, forest-temperature and forest-soil relationships. Eleven forest type groups mainly representing the Gulf Coast forests were used to present the fitting of GWR models. The model performance was spatially accessed using maps and measured by R² values, adjusted R² values, Akaike information criterion (AIC), and residual sum of squares (RSS).