Though forest ecosystems play a critical role in enhancing ecological, environmental, economic, and societal sustainability, on a global scale, their future outlooks are uncertain given the wide-ranging threats they are exposed to. The uniqueness of this study is to provide a line of evidence in which forest change trajectories are not only tracked but also evaluated through the lenses of forestry and economic oriented events’ timelines. The dynamics of forest change trajectories were mined using a temporal model. To understand the forces driving the changes, the change trajectories were linked to the timelines when forestry policies and economic factors where adopted. During 1980–1990, the forest change trajectory assumed a peak (forest gain). This was interpreted as a response to the adoption of policies that promoted ecological conservation. During 1995–2010, the forest change trajectories reflected the response to the antagonistic effects of forest-oriented policies and the economy-oriented drivers. During 2010–2015, the forest change trajectories assumed a deep (forest loss). This was attributed as a response to the economy-oriented factors. However, inferences from the results indicated that deforestation driven by economic factors was restricted by forest management policies. Though the role of economic factors has promoted developments within the study area, forest policies still constrain illegal logging and play a key role in protecting forests. We hope that insights from this study will inform, support and guide decisions for precise and smart sustainable forest management plans.
In arid zones, ecological barriers can affect the agricultural development and sustainable development of oases. Therefore, balancing cropland and desert vegetation is the key to realizing ecological and rural revitalization. Then, we proposed a theoretical framework from the perspective of rural revitalization to analyze the key drivers that affect the balance of cropland and desert vegetation during 1990-2020. The results showed that cropland area in Xinjiang decreased by 881 km 2 during 1990-1995, while it continually increased by 34,086 km 2 during 1995-2020. However, grassland and woodland increased by 8,086 km 2 during 2015-2020, indicating that the balance between cropland gain and desert vegetation loss began to improve. Most cropland expansions were reclaimed from woodland/grassland and unused land. During 1990-2005, changes between cropland and unused land in Xinjiang were mainly affected by gross domestic product (GDP). GDP and population most significantly affected the conversion (17,939 km 2 ) of woodland/grassland to cropland during 1995-2015. Although population and economic growth led to an increase in cropland, the implementation of simultaneous and consistent policies on ecological conservation offset the reduction in desert vegetation. The findings can provide suggestions for smart cropland management and rural
Aims We explored α and β species diversity, functional diversity and phylogenetic diversity distribution patterns in three tropical cloud forests along environmental gradients in air temperature and precipitation. Methods We sampled plots in three tropical cloud forests which are located in the west (Bawangling, 21 plots, BWL), the southwest (Jianfengling, 12 plots, JFL), and the central of Hainan Island (Limushan, 15 plots, LMS). We collected species data and functional trait data including plant height, specific leaf area, chlorophyll content, leaf thickness and wood density. We assessed the differences within-and among-community species diversity, functional diversity and phylogenetic diversity in these three tropical cloud forests using the Kruskal-Wallis test. Important findings The tropical cloud forests in JFL had the highest species abundance and richness whereas the lowest in LMS. However, the Bray-Curtis and Jaccard dissimilarity coefficients showed the opposite distribution patterns (i.e. the highest in LMS whereas the lowest in BWL). Distinct distribution patterns in species diversity across the three tropical cloud forests may be explained by the air temperature and relative humidity. The functional evenness (FEve) within communities was the highest while functional richness (FRic), Rao's quadratic
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