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Aim: We construct a framework for mapping pattern and drivers of insect diversity at the continental scale and use it to test whether and which environmental gradients drive insect beta diversity. Location: Global; North and Central America; Western Europe. Time period: 21st century. Major taxa studied: Insects. Methods: An informatics system was developed to integrate terrestrial data on insects with environmental parameters. We mined repositories of data for distribution, climatic data were retrieved (WorldClim), and vegetation parameters inferred from remote sensing analysis (MODIS Vegetation Continuous Fields). Beta diversity between sites was calculated and then modeled with two methods, Mantel test with multiple regression and generalized dissimilarity modeling. Results: Geographic distance was the main driver of insect beta diversity. Independent of geographic distance, bioclimate variables explained more variance in dissimilarity than vegetation variables, although the particular variables found to be significant were more consistent in the latter, particularly, tree cover. Tree cover gradients drove compositional dissimilarity at denser coverages, in both continental case studies. For climate, gradients in temperature parameters were significant in driving beta diversity more so than gradients in precipitation parameters. Main conclusions: Although environmental gradients drive insect beta diversity independently of geography, the relative contribution of different climatic and vegetational parameters is not expected to be consistent in different study systems. With further incorporation of additional temporal information and variables, this approach will enable the development of a predictive framework for conserving insect biodiversity at the global scale. [ABSTRACT FROM AUTHOR]

Expansion of rubber plantations into sub-optimal environments has been a dominating land conversion in continental South-East Asia in the last decade. Regional evaluation of the carbon sequestration potential of rubber trees depends largely on the selection of suitable allometric equations and the biomass-to-carbon conversion factor. Most equations are age-, elevation-, or clone-specific, and their application therefore gives uncertain results at the landscape level with varying age groups, elevation ranges, and clone types. Currently, for rubber-based systems, none of the allometric equations takes environmental factors (e.g. climate, topographic condition, soil properties, and management scheme) into consideration to allow pan-tropical usage. Against this background, 30 rubber trees with a root profile of up to 2 m were destructively harvested and 882 rubber trees were measured non-destructively in 27 plots, covering rotation lengths of 4–35 years, elevation gradients of 621–1127 m, and locally used clone types (GT1, PRIM600, Yunyan77-4) in mountainous South Western China. Allometric equations for aboveground biomass ( AGB ) estimations considering diameter at breast height ( DBH ), tree height ( H ), and wood density were superior to other equations. A simpler model with similar performance ( AGB = 0.0419 DBH 2.316 H 0.478 ) can be used if tree-specific wood density is not available. For belowground biomass ( BGB ) a model using only DBH can provide a robust prediction ( BGB = 0.207 DBH 1.668 ). We also tested goodness of fit for the recently proposed pan-tropical forest model, which includes a bioclimatic factor E , combining indices of temperature and precipitation variability and drought intensity. Prediction of AGB by the model calibrated with the harvested rubber tree biomass and wood density was more accurate than the results produced by the pan-tropical forest model adjusted to local conditions. The relationships between DBH and height and between DBH and biomass were influenced by tapping, therefore biomass and C stock calculations for rubber have to be done using species-specific allometric equations. Based on the analysis of environmental factors acting at the landscape level, we found that above- and belowground carbon stocks were mostly affected by stand age, soil clay content, aspect, and planting density. Increasing planting density to >570 trees per ha according to the regional plantation management strategy had a negative impact on aboveground carbon stock of old rubber plantations. The integration of bioclimatic and regional management factors is a further approach to build widely applicable biomass models for pan-tropical rubber-based systems. The results of this study provide reference for reliable carbon accounting in other rubber-cultivated regions. [ABSTRACT FROM AUTHOR]

Tropical lowland rainforests are increasingly threatened by the expansion of agriculture and the extraction of natural resources. In Jambi Province, Indonesia, the interdisciplinary EFForTS project focuses on the ecological and socio-economic dimensions of rainforest conversion to jungle rubber agroforests and monoculture plantations of rubber and oil palm. Our data confirm that rainforest transformation and land use intensification lead to substantial losses in biodiversity and related ecosystem functions, such as decreased above- and below-ground carbon stocks. Owing to rapid step-wise transformation from forests to agroforests to monoculture plantations and renewal of each plantation type every few decades, the converted land use systems are continuously dynamic, thus hampering the adaptation of animal and plant communities. On the other hand, agricultural rainforest transformation systems provide increased income and access to education, especially for migrant smallholders. Jungle rubber and rubber monocultures are associated with higher financial land productivity but lower financial labour productivity compared to oil palm, which influences crop choice: smallholders that are labour-scarce would prefer oil palm while land-scarce smallholders would prefer rubber. Collecting long-term data in an interdisciplinary context enables us to provide decision-makers and stakeholders with scientific insights to facilitate the reconciliation between economic interests and ecological sustainability in tropical agricultural landscapes. [ABSTRACT FROM AUTHOR]

The rapidly growing car industry in China has led to an equally rapid expansion of monoculture rubber in many regions of South East Asia. Xishuangbanna, the second largest rubber planting area in China, located in the Indo-Burma biodiversity hotspot, supplies about 37% of the domestic natural rubber production. There, high income possibilities from rubber drive a dramatic expansion of monoculture plantations which poses a threat to natural forests. For the first time we mapped rubber plantations in and outside protected areas and their net present value for the years 1988, 2002 (Landsat, 30 m resolution) and 2010 (RapidEye, 5 m resolution). The purpose of our study was to better understand the pattern and dynamics of the expansion of rubber plantations in Xishuangbanna, as well as its economic prospects and conservation impacts. We found that 1) the area of rubber plantations was 4.5% of the total area of Xishuangbanna in 1988, 9.9% in 2002, and 22.2% in 2010; 2) rubber monoculture expanded to higher elevations and onto steeper slopes between 1988 and 2010; 3) the proportion of rubber plantations with medium economic potential dropped from 57% between 1988 and 2002 to 47% in 2010, while the proportion of plantations with lower economic potential had increased from 30% to 40%; and 4) nearly 10% of the total area of nature reserves within Xishuangbanna has been converted to rubber monoculture by 2010. On the basis of our findings, we conclude that the rapid expansion of rubber plantations into higher elevations, steeper terrain, and into nature reserves (where most of the remaining forests of Xishuangbanna are located) poses a serious threat to biodiversity and environmental services while not producing the expected economic returns. Therefore, it is essential that local governments develop long-term land use strategies for balancing economic benefits with environmental sustainability, as well as for assisting farmers with the selection of land suitable for rubber production. [ABSTRACT FROM AUTHOR]

Hemispherical canopy photography is a widely used technique to observe crown-related forest variables. However, standardization of this technique remains challenging, as exposure and threshold settings continue to constitute the main sources of variation of such photographs. This paper presents a new method to overcome standardization issues by using thermal canopy photography. Using a thermal camera, images are produced which are not critically limited in their dynamic range so that photographic exposure becomes irrelevant. Moreover, the high temperature contrast between "sky" and "non-sky", resulting from extreme low sky temperatures, facilitates the unambiguous selection of a threshold which separates "sky" from "non-sky" pixels. For a comparison, we have taken canopy images with a high-resolution thermal camera (VarioCam hr head - Infratec, Dresden, Germany) and an optical camera (Nikon D70s). The correlation of canopy closure values derived from the image pairs was r = 0.98. Our findings thus show that thermal canopy photography is a promising and simple to use alternative to optical canopy photography, because it limits possible sources of variability, since exposure settings and threshold definition cease to be an issue. [ABSTRACT FROM AUTHOR]

Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy and mass exchanges in the biosphere/ atmosphere system. In this article we assess the applicability of the RapidEye satellite system, whose sensor is optimized towards vegetation analyses, for mapping LAI along a disturbance gradient, ranging from heavily disturbed shrub land to mature mountain rainforest. By incorporating image texture features into the analysis, we aim at assessing the potential quality improvement of LAI maps and the reduction of uncertainties associated with LAI maps compared to maps based on Vegetation Indexes (VI) solely. We identified 22 out of the 59 image features as being relevant for predicting LAI. Among these, especially VIs were ranked high. In particular, the two VIs using RapidEye's RED-EDGE band stand out as the top two predictor variables. Nevertheless, map accuracy as quantified by the mean absolute error obtained from a 10-fold cross validation (MAE_CV) increased significantly if VIs and texture features are combined (MAE_CV = 0.56), compared to maps based on VIs only (MAE_CV = 0.62). We placed special emphasis on the uncertainties associated with the resulting map addressing that map users often treat uncertainty statements only in a pro-forma manner. Therefore, the LAI map was complemented with a map depicting the spatial distribution of the goodness-of-fit of the model, quantified by the mean absolute error (MAE), used for predictive mapping. From this an area weighted MAE (= 0.35) was calculated and compared to the unweighted MAE of 0.29. Mapping was done using randomForest, a widely used statistical modeling technique for predictive biological mapping. [ABSTRACT FROM AUTHOR]

Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vegetation. A wide range of automatic thresholding or binarization algorithms exists to classify the photographs. The variety in methodology hampers ability to compare results across studies. To identify an optimal threshold selection method, this study assessed the accuracy of seven binarization methods implemented in software currently available for the processing of hemispherical photographs. Therefore, binarizations obtained by the algorithms were compared to reference data generated through a manual binarization of a stratified random selection of pixels. This approach was adopted from the accuracy assessment of map classifications known from remote sensing studies. Percentage correct () and kappa-statistics () were calculated. The accuracy of the algorithms was assessed for photographs taken with automatic exposure settings (auto-exposure) and photographs taken with settings which avoid overexposure (histogram-exposure). In addition, gap fraction values derived from hemispherical photographs were compared with estimates derived from the manually classified reference pixels. All tested algorithms were shown to be sensitive to overexposure. Three of the algorithms showed an accuracy which was high enough to be recommended for the processing of histogram-exposed hemispherical photographs: “Minimum” ( 98.8%; 0.952), “Edge Detection” ( 98.1%; 0.950), and “Minimum Histogram” ( 98.1%; 0.947). The Minimum algorithm overestimated gap fraction least of all (11%). The overestimation by the algorithms Edge Detection (63%) and Minimum Histogram (67%) were considerably larger. For the remaining four evaluated algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an incompatibility with photographs containing overexposed pixels was detected. When applied to histogram-exposed photographs, these algorithms overestimated the gap fraction by at least 180%. [ABSTRACT FROM AUTHOR]

Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy and mass exchanges in the biosphere/ atmosphere system. In this article we assess the applicability of the RapidEye satellite system, whose sensor is optimized towards vegetation analyses, for mapping LAI along a disturbance gradient, ranging from heavily disturbed shrub land to mature mountain rainforest. By incorporating image texture features into the analysis, we aim at assessing the potential quality improvement of LAI maps and the reduction of uncertainties associated with LAI maps compared to maps based on Vegetation Indexes (VI) solely. We identified 22 out of the 59 image features as being relevant for predicting LAI. Among these, especially VIs were ranked high. In particular, the two VIs using RapidEye's RED-EDGE band stand out as the top two predictor variables. Nevertheless, map accuracy as quantified by the mean absolute error obtained from a 10-fold cross validation (MAE_CV) increased significantly if VIs and texture features are combined (MAE_CV = 0.56), compared to maps based on VIs only (MAE_CV = 0.62). We placed special emphasis on the uncertainties associated with the resulting map addressing that map users often treat uncertainty statements only in a pro-forma manner. Therefore, the LAI map was complemented with a map depicting the spatial distribution of the goodness-of-fit of the model, quantified by the mean absolute error (MAE), used for predictive mapping. From this an area weighted MAE (= 0.35) was calculated and compared to the unweighted MAE of 0.29. Mapping was done using randomForest, a widely used statistical modeling technique for predictive biological mapping. [ABSTRACT FROM AUTHOR]

At least 10 different methods to determine exposure for hemispherical photographs were used by scientists in the last two decades, severely hampering comparability among studies. Here, an overview of the applied methods is reported. For the standardization of photographic exposure, a time-consuming reference measurement in the open land towards the unobstructed sky was required so far. The two Histogram Methods proposed here make use of the technical advances of digital cameras which enable users to assess a photograph's histogram directly at the location of measurement. This avoids errors occurring due to variations in sky lighting happening in the time span between taking the reference measurement and reaching the sample location within the forest. The Histogram Methods speed up and simplify taking hemispherical photographs, and introduce an objectively applicable, standardized approach. We highlight the importance of correct exposure by quantifying the overestimation of gap fraction resulting from auto-exposed photographs under a wide range of canopy openness situations. In our study, gap fraction derived from auto-exposed photographs reached values up to 900% higher than those derived from non-overexposed photographs. By investigating the size of the largest gap per photograph and the number of small gaps (gaps contributing less than 0.1% to gap fraction), we concluded that the overestimation of gap fraction resulted mainly from the overexposure of vegetation surrounding large gaps. [ABSTRACT FROM AUTHOR]

Abstract: Rapid land-use transformations are occurring throughout the Mekong Region, and especially in Xishuangbanna, Yunnan Province in southwest China. Most of this is due to the spread of monoculture rubber plantations. Using a new map derived from Landsat and RapidEye imagery tracking the spread of rubber from 1992 to 2010 in combination with a literature review and interviews with key local experts and officials, we performed a general overview of the extent, causes and consequences of landscape transformation in Xishuangbanna. We discovered that structural and functional biodiversity has been reduced, habitat fragmentation has increased, carbon sequestration in natural forests has been reduced, and hydrological systems altered. For humans, while incomes have risen, food insecurity has also grown. The drivers of these changes are regional economic integration, government policy, and conservation vs development value systems. To improve land-use management, we surveyed the availability of ecological and socioeconomic indicators that may better track such changes. We found that combining both types of indicators within a multi-scale conservation planning framework would help to inform policy making in the region. As yet, however, there is little integrative research using indicators to track changes in ecosystems and human livelihoods in the region. [Copyright &y& Elsevier]

Highlights: [•] Xishuangbanna Prefecture, Yunnan, in southwest China, is renowned for rich biodiversity, both natural and within traditional agroecosystems. [•] From 2002 to 2012 the area under rubber plantations has increased from 8% to 22%. [•] Rubber currently occupies most of the area where climatic conditions are conducive to rubber production. [•] Rubber production is limited by colder climatic conditions at higher elevations. [•] By 2050, 75% of Xishuangbanna will become conducive to rubber production. [Copyright &y& Elsevier]