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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., An informatics system was developed to integrate terrestrial data on insects with environmental parameters and to infer biogeographic maps. Beta diversity was driven by geographic, climatic, and vegetation gradients.

Heterogeneity in forest structure, naturally occurring or induced by management, is continuous in space and time. However, measures used to quantify structure of forests are scale-variant, as they rely on bounded observations on either ecological or forest inventory observation units. The understanding of the influence of the scale of observation in mapping of forest structural heterogeneity is limited. Therefore, we researched into effects of plot size on quantifying forest structural heterogeneity, where we describe heterogeneity by three indices in stands under different management systems. In addition, we studied the performance of structural indices in separating different forest management systems across plot sizes, and created wall to wall maps of the indices using airborne LiDAR metrics describing the vertical distribution of canopy heights at different scales of observation. The studied indices are: Gini Coefficient (GC), Structural Complexity Index (SCI), and Enhanced Structural Complexity Index (ESCI). SCI and ESCI require fully mapped plots whereas GC has no information on individual tree locations. Inventory data from 95 one-hectare plots covering a range of management intensities from un-managed to age class forests were used. We quantified the three structural indices for 18 plot sizes ranging from 225 m2 to 10,000 m2. Linear fixed effects models were used to study the effects of plot sizes in different levels of structural heterogeneity and Random Forest (RF) models used to provide wall-to-wall maps at varying scales from airborne LiDAR data. The simulation showed that all indices were influenced by the scale of observation with larger effects for plots in forests with higher structural heterogeneity. For the data analyzed we found a threshold scale for enumerating stand structural heterogeneity between 900 m2 and 2500 m2. However, stable field and remote sensing predictions of stand structural heterogeneity required plots at least ⩾ 2500 m2. Compared to GC, SCI and ESCI improved separation of forest structure in the three management systems and at all observed scales. A change of plot sizes affected bivariate relationships between structural indices and airborne LiDAR metrics as well as the resultant predictive models. Smaller plot sizes yielded weaker relationships and predictive models. All structure indices were predicted from airborne LiDAR with RMSE ⩽ 22 % at scales equal or larger than the identified threshold plot size. These findings are relevant to optimize plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Additionally, derived maps are useful for studies on forest structure and the link with forest growth, degradation, management intensity, productivity, and biodiversity in the regions.

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, therefore, presents a new method to overcome standardization issues by using thermal canopy photography. With 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 our 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.

Understanding and managing pollination service is hindered by taxonomic impediments and paucity of data, particularly in the tropics. Herein we apply integrative species delineation and taxonomy to test impacts of land use on the diversity of bee communities within Xishuangbanna (Yunnan, south China), a highly biodiverse tropical region which has undergone extensive land conversion to rubber plantation. 128 Operational Taxonomic Units (OTU) were inferred by an iterative and integrative approach. Bee activity differed significantly across land use samples, although community composition corresponded more to level of vegetation density, when accounting for spatial structure. Species diversity was high in young rubber plantations, although composition overlapped with other species-rich habitats (natural forest edge and river banks), and older plantations (>8 years) showed very low diversity under all measures. Community structures were similar between the natural forest interior and edge, although analysis indicated contrasting drivers of diversity, with clustering in the interior and overdispersion in the forest edge. Further, phylogenetic diversity and derived indices were underestimated when reference data were omitted from analysis. The description of bee communities herein permits more informed choices in land management with respect to ensuring continuation of essential services by bees.

All rubber tree clones (Hevea brasiliensis) exhibit regular annual wintering characterized by senescence and abscission of leaves. After 3–4 weeks, this is followed by the onset of new leaves. It is likely that the timing of leaf onset affects the susceptibility of rubber trees to rubber powdery mildew disease, as this predominantly infests young leaves. However, little information is available on the phenological behavior of different rubber clones, or how meteorological factors affect such behavior. We assessed the wintering and flowering patterns of five rubber clones in Xishuangbanna, southwest China, based on observations made from 1978 to 2011, and evaluated how these patterns responded to different meteorological factors. Partial least squares regression was used to analyze the timing of defoliation, refoliation, and flowering. Our results showed that the two clones RRIM 600 and GT1 defoliated during the last week of December and refoliated in the last week of January, and clones Yunyan 277-5, Yunyan 34-4, and PR 107 defoliated during the first week of January and refoliated in the second week of February. The number of hours of sunshine during both the rainy season and the cold dry period in the dry season were important determinants of phenological changes in the rubber trees. Similarly, higher temperatures tended to delay the onset of defoliation and refoliation, and were a triggering factor for the onset of flowering. These results may help rubber cultivators to schedule appropriate disease control measures, as well as to design hybridization programs aiming at the production of clones which are resistant to foliar disease.

Previous research found that phylogenetic clustering increased with disturbance for tropical trees, suggesting that community assembly is mainly influenced by abiotic factors during early succession. Lianas are an important additional component of tropical forests, but their phylogenetic community structure has never been investigated. Unlike tropical trees, liana abundance is often high in disturbed forests and diversity can peak in old secondary forest. Therefore, phylogenetic structure along a disturbance gradient might also differ from tropical tree communities. Here we determined phylogenetic community structure of lianas along a disturbance gradient in a tropical montane forest in China, using the net relatedness index (NRI) from 100 equivalent phylogenies with varying branch length that were constructed using DNA-barcode sequences. Three additional phylogenetic indices were also considered for comparison. When NRI was used as index phylogenetic clustering of liana communities decreased with decreasing tree basal area, suggesting that liana competitive interactions dominate during early succession, which is in contrast to the pattern reported for trees. Liana communities in mature forests, on the other hand, were phylogenetic clustered, which could be caused by dispersal limitation and/or environmental filtering. The three additional phylogenetic indices identified different, sometimes contradicting predictors of phylogenetic community structure, indicating that caution is needed when generalizing interpretations of studies based on a single phylogenetic community structure index. Our study provides a more nuanced picture of non-random assembly along disturbance gradients by focusing on a non-tree forest component.

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.

An analysis and multi-model approach, based on a statistically derived Global Environmental Stratification (GEnS) and using a downscaled ensemble (n = 63) of CIMP5 Earth System Models applied across four representative concentration pathways (RCP), has been used to project the impact of climate change on spatial distribution of bioclimatic zones and ecosystems within the biodiverse rich Xishuangbanna Prefecture, Yunnan Province, by the year 2050. Four bioclimatic zones and 9 strata were identified, overlaid with protected areas, and associated with on-going landuse change, i.e. a rapid increase in rubber plantation from 8% to 22% of total area between 2002 and 2010. Significant changes in the areal extent and distribution of all zones and strata are projected, with an averaged mean annual temperature increase ranging from 1.6 degrees C to 2.4 degrees C By 2050, there are significant geographical shifts in all identified strata, with an average upward shift of 309 m of elevation for all strata. On average, more than 75% of Xishuangbanna is predicted to shift to a different zone, with 96% shifting to a different stratum. The area conducive to rubber plantations, currently limited by climatic conditions, expands to nearly 75% of the total area. Climatic change potentially removes the bioclimatic barriers to further expansion of rubber plantations within the area and increases pressure on remaining biodiversity both within and outside of protected areas. The analysis provides the basis for understanding potential impacts of changing bioclimatic conditions on managed and unmanaged ecosystems and landuse change trends, within the context of ongoing rapid change and agricultural expansion in the area. Current efforts to conserve forests, biodiversity and traditional landuse systems require an improved understanding of both the projected climatic changes and the responses of biodiversity and traditional agricultural systems to changing conditions. (C) 2013 Elsevier Ltd. All rights reserved.

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. (C) 2012 Elsevier Ltd. All rights reserved.

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.

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.

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. peerReviewed

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 ([Formula: see text]) and kappa-statistics ([Formula: see text]) 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" ([Formula: see text] 98.8%; [Formula: see text] 0.952), "Edge Detection" ([Formula: see text] 98.1%; [Formula: see text] 0.950), and "Minimum Histogram" ([Formula: see text] 98.1%; [Formula: see text] 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%. Open Access Publikationsfonds 2014 peerReviewed

The horizontal distribution of stems, stand density and the differentiation of tree dimensions are among the most important aspects of stand structure. An increasing complexity of stand structure is often linked to a higher number of species and to greater ecological stability. For quantification, the Structural Complexity Index (SCI) describes structural complexity by means of an area ratio of the surface that is generated by connecting the tree tops of neighbouring trees to form triangles to the surface that is covered by all triangles if projected on a flat plane. Here, we propose two ecologically relevant modifications of the SCI: The degree of mingling of tree attributes, quantified by a vector ruggedness measure, and a stem density term. We investigate how these two modifications influence index values. Data come from forest inventory field plots sampled along a disturbance gradient from heavily disturbed shrub land, through secondary regrowth to mature montane rainforest stands in Mengsong, Xishuangbanna, Yunnan, China. An application is described linking structural complexity, as described by the SCI and its modified versions, to changes in species composition of insect communities. The results of this study show that the Enhanced Structural Complexity Index (ESCI) can serve as a valuable tool for forest managers and ecologists for describing the structural complexity of forest stands and is particularly valuable for natural forests with a high degree of structural complexity. peerReviewed

Deforestation and forest degradation are driving unprecedented declines in biodiversity across the tropics, and understanding the consequences of these changes for ecosystem functioning is essential for human well-being. Forest degradation and loss alter ecosystem functioning through changes in species composition and abiotic conditions. However, the consequences of these changes for heterospecific processes are often poorly understood. Leaf litter decomposition is a major source of atmospheric carbon and critical for carbon and nutrient cycling. Through a highly replicated litter-bag experiment (3360 bags), we quantified the effects of litter quality, decomposer functional diversity and seasonal precipitation regime on litter decomposition along a tropical disturbance gradient in SW China. In addition, using soil and litter from sites selected from across the disturbance gradient, we established replicated litter-bed treatments and exposed these to a gradient of simulated canopy cover in a shadehouse. Across the landscape, mass loss from litter-bags after 12 months varied from 7% to 98%. Even after 12 months, litter-bags installed at the beginning of the dry season had much lower mass loss than those installed at the beginning of the wet season. As expected, litter quality and faunal exclusion had substantial effects on decomposition rates. Decomposition rates declined along the disturbance gradient from mature forest, through regenerating forest to open land, although the effect size was strongly dependent on installation season. The effect of excluding meso- and macro-invertebrates increased with increasing forest degradation, whereas the effect of litter quality declined. Results from the shade-house experiment strongly suggested that forest degradation effects were driven predominantly by changes in micro-climatic conditions resulting from increased canopy openness. To better model the impacts of anthropogenic global change on litter decomposition rates, it will be important to consider landscape scale processes, such as forest degradation. peerReviewed