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The expansion of rubber plantations in northern Southeast Asia over the last 20 years displaced shifting cultivation and tropical forests. In Xishuangbanna, SW China, rubber occupied 22% of the area by 2010, reducing lowland forest to scattered fragments, with severe impacts on plants, animals, and ecosystem services. The rubber price has declined steeply since 2011, but consequences for forest biodiversity have not previously been explored. We use a new approach for vegetation mapping, combining phenological information with object-based classification, to produce land-use maps for 2002, 2010, 2014, and 2018. During 2002–2018, forest cover declined continuously, from 71 to 52% of the land area, while rubber increased from 11 to 24% by 2014, before declining to 21% by 2018. Other farmlands also declined while tea plantations increased. Forest patch number increased 8-fold during 2001–2014, while patch size decreased 10-fold, but these trends were partly reversed after 2014, with the loss of numerous small ( 10 ha) patches in 2018 were forest throughout the study period, but many smaller patches are secondary. Currently, 20% of Xishuangbanna is in protected areas. Unprotected forest is disproportionately on steep slopes at high altitudes, while biodiversity is highest in valleys and at low altitudes, where only smaller patches remain, creating challenges for conservation planning. We recommend that all the largest patches are preserved, plus smaller patches selected for their biota, and that forest restoration is used to restore connectivity and buffer small patches of high conservation value.

International audience; Rubber powdery mildew disease (Oidium heveae) is a serious threat to natural rubber production (Hevea brasiliensis) in some rubber developing regions of the world. Both phenological- and meteorological-related factors have been reported influencing the powdery mildew disease. However, few studies have investigated the effects of both phenological- and meteorological-related factors on the disease. The objective of this study is to quantify the contributions of phenological- and meteorological-related factors to affect the disease. We used the partial least squares (PLS) regression method to comprehensively quantify the effects of thirty-five phenological related factors and six meteorological factors on the infection level of powdery mildew of rubber trees over 9-year records (2003–2011). The relative contributions of significant factors were further investigated by the variation partition analysis. We found that the most influential variables were the mean temperature during winter and the duration of leaf development to maturation which explained 32 and 26% of the variations in the infection level. We found the controlling role of winter mean temperature, for the first time, on the infection level of powdery mildew. The controlling role of winter temperature may have directly increase the infection level when winter temperature is high and indirectly increase the infection level through prolonging the duration of leaf development to maturation, although the duration itself had smaller influences. We detected a warming trend of the winter temperatures from 2003 to 2011, which indicates that the infection level of powdery mildew will be increased if the winter warming continues.

International audience; Rubber powdery mildew caused by the foliar fungiOidium heveaeis one of the main diseases affecting rubber plantations (Hevea brasiliensis) worldwide. It is particularly serious in sub-optimal growing areas, such as Xishuangbanna in SW China. To prevent and control this disease, fungicides causing serious environmental problems are widely used. Strong correlations between the infection level and the temperature variables were reported previously, but they were related to monthly data that did not allow unraveling the patterns during the entire sensitive period. We correlated the infection level of powdery mildew of rubber trees recorded over 2003-2011 with antecedent 365 days daily temperature variables using partial least squares (PLS) regression. Our PLS regression results showed that the infection level of powdery mildew responded differently to the temperature variables of the defoliation and refoliation periods. Further analysis with Kriging interpolation showed that the infection level increased by 20% and 11%, respectively, per 1 degrees C rise of the daily maximum and mean temperature in the defoliation season, while it decreased by 8% and 10%, respectively, per 1 degrees C rise of the daily maximum and temperature difference in the refoliation season. This pattern was likely linked to the effects of temperature on leaf phenology. It seems highly possible that the infection level of powdery mildew increases, as increasing trends of maximum temperature and mean temperature during the defoliation continue.

Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area.

The term forest transition refers to a change in forest cover over a given area from a period of net forest area loss to a period of net gain. Whether transitioning from deforestation to reforestation can lead to improved ecosystem services, depends on the quality and characteristics of the newly established forest cover. Using publicly available data, we examine forest transition in two regions of tropical China: Hainan Island and Xishuangbanna. We found that the overall increase of forest cover in both areas during the 1980s was due to an increase in plantation forests rather than to increases in the area covered by natural forest. We also found a time lag between the increase in overall forest cover and an increase in natural forest. On Hainan Island, natural forest continued to decline beyond the point in time when overall forest cover had started to increase, and only began to recover ten years after the turning point in 1978. In Xishuangbanna, where the transition point occurred ten years later, the decline of natural forest cover is still going on. These divergent trends underlying forest transition are concealed by the continued practice to apply the term “forest” broadly, without distinguishing between natural forests and planted forests. Due to the use of undiscriminating terminology, the loss of natural forest may go unnoticed, increasing the risk of plantation forests displacing natural forests in the course of forest transition. Our findings are important for programs related to forest management and ecosystem services improvement, including reforestation and Payments for Ecosystem Services programs.

The Returning Farmland to Forest Program (RFFP; tuigeng huanlin gongcheng), which compensates farmers for cultivating forest on previously non-forested land, is central among the “ecological construction” programs that have transformed landscapes across China. Under the RFFP, state authorities have redefined large areas of land as afforestation area intended to provide environmental services including erosion control, carbon sequestration, and biodiversity regeneration. Tensions among the program’s social and environmental objectives as well as the combination of formal rigidity and practical flexibility manifest in ambiguous forest classifications and tremendous variation in outcomes. The RFFP centers on two forest categorizations: ecological forest, intended to rehabilitate environmental services, and commercial forest, which may provide lesser ecological benefits but delivers more immediate economic benefits to households. RFFP policy documents designate “dual-function species” that can be planted as “forests with a primary goal of ecological service provision which also generate commercial value.” A substantial portion of “ecological forests” are dual landscapes intended to accomplish both environmental and socioeconomic goals. Drawing from cases in Yunnan, Sichuan, and Hainan provinces, this paper examines the processes of central policy-making and local implementation through which dual-function forests have proliferated, showing how rural residents have responded to the risks and promises of dual-function forests and exploring implications for rural livelihoods and landscapes. While they might be expected to bring win-win outcomes, the performance of dual-function forests on social and environmental goals depends on how local officials and residents respond to the program. Their proliferation demonstrates tensions inherent in state projects aimed at generating both environmental and economic values from rural landscapes.

A seasonal calendar, based on traditional knowledge of ecological indicators, seasonal variations and associated activities, can provide a baseline for understanding the practices of indigenous along with climatic variation. This paper investigates the ethno-ecological knowledge of indigenous people in Taxkorgan regarding the use of ecological cues to conduct seasonal activities that harmonize with climatic variations. Meteorological data from the nearest station was used to understand climatic variations and develop indices. The results revealed that indigenous elders still adopt traditional methods to decide the time of various annual activities observing and using seasonal cues, such as the height and color of grass, the arriving of migratory birds and phenological observations. Moreover, same or diverse indicators were used at settlements located in different elevations. The analysis revealed that the region was recently getting warmer and wetter compared to previous decades, and local perceptions were matched with climatic recordings. Local inhabitants already practiced earlier plantation of crops (e.g., wheat) in recent years. Climatic indices calculated revealed and validated recent weather condition can support earlier plantation of crops. Hence, the strong forecasting system using meteorological evidence to support existing local knowledge on ecological indicators and adjust seasonal calendars can improve indigenous people&rsquo, s abilities to cope with climate risks. Furthermore, this can support in developing adaptation schemes that respond to community needs. The approaches and findings can be used to facilitate the management of these natural resource based on the adaptive framework and to create data that can be tested in subsequent studies.

Invasive plants readily invade metal-contaminated areas. The hyperaccumulation of toxic heavy metals is not an uncommon feature among plant species. Although several hypotheses were proposed to explain this phenomenon, it is currently unclear how hyperaccumulation may benefit plants. The invasive Crofton weed (Ageratina adenophora) is a known hyperaccumulator of chromium and lead. We previously found that the species can also hyperaccumulate cadmium. The role of phytoaccumulation in defense to pathogen attack is unclear. We inoculated A. adenophora plants with a common generalist pathogen (Rhizoctonia solani) to test its resistance under cadmium treatment. We found evidence that cadmium hyperaccumulation reduced pathogen infection in A. adenophora. Our findings indicate elemental defense is highly cost efficient for hyperaccumulators inhabiting metal-contaminated sites, where plants were only modestly affected by cadmium. The reduction in pathogen damage conferred by cadmium was relatively high, particularly under lower cadmium levels. However, the benefits at higher levels may be capped. Elemental defense may be a key mechanism for plant invasion into polluted sites, especially in regions with widespread industrial activity. Our study highlights the importance of testing different metal concentrations when testing plant resistance and the importance of considering enemy attack when selecting plants for phytoremediation.

The phenology of rubber trees (Hevea brasiliensis) could be influenced by meteorological factors and exhibits significant changes under different geoclimates. In the sub-optimal environment in Xishuangbanna, rubber trees undergo lengthy periods of defoliation and refoliation. The timing of refoliation from budburst to leaf aging could be affected by powdery mildew disease (Oidium heveae), which negatively impacts seed and latex production. Rubber trees are most susceptible to powdery mildew disease at the copper and leaf changing stages. Understanding and predicting leaf phenology of rubber trees are helpful to develop effective means of controlling the disease. This research investigated the effect of several meteorological factors on different leaf phenological stages in a sub-optimal environment for rubber cultivation in Jinghong, Yunnan in Southwest China. Partial least square regression was used to quantify the relationship between meteorological factors and recorded rubber phenologies from 2003 to 2011. Minimum temperature in December was found to be the critical factor for the leaf phenology development of rubber trees. Comparing the delayed effects of minimum temperature, the maximum temperature, diurnal temperature range, and sunshine hours were found to advancing leaf phenologies. A comparatively lower minimum temperature in December would facilitate the advancing of leaf phenologies of rubber trees. Higher levels of precipitation in February delayed the light green and the entire process of leaf aging. Delayed leaf phenology was found to be related to severe rubber powdery mildew disease. These results were used to build predictive models that could be applied to early warning systems of rubber powdery mildew disease.