Classification of rhizosphere components using visible–near infrared spectral images

To establish new techniques for automatic classification of rhizosphere components, we investigated the utility of visible (VIS) and near–infrared (NIR) spectral images of the rhizosphere under two soil moisture conditions (mean volumetric water content: 0.39 and 0.16 cm³ cm^?3). Spectral reflectance images of the belowground parts of hybrid poplar cuttings (Populus deltoides × P. euramericana, I45/51) grown in a rhizobox were recorded at 120 spectral bands ranging from 480 to 972 nm. We examined which wavelengths were suitable and the number of spectral bands needed to accurately classify live roots of four age classes, dead roots, leaf mold, and soil. VIS reflectance (<700 nm) of live roots first increased and then decreased with age, whereas NIR reflectance (≥700 nm) was stable in mature roots. The reflectance of dead roots was lower than that of mature roots in both the VIS and NIR spectral regions. VIS reflectance did not differ among dead roots, leaf mold, and soil, but the NIR reflectance was clearly lower in soil than in the other materials. The reflectance of leaf mold and soil increased mainly in the NIR spectral region with reducing soil moisture, but this increase did not affect the order of reflectance intensity among the rhizosphere components in general. Although the most suitable spectral bands statistically selected for classifying rhizosphere components differed somewhat between moist and dry conditions, the spectral bands 580–679 nm (VIS) and 848–894 nm (NIR) provided high reliability under both conditions. Classification accuracy was higher when using two to five VIS–NIR images (overall accuracy ≥87.8%) than three VIS images (red, green, and blue; accuracy <67.1%). The high accuracy with VIS–NIR was mainly due to successful separation of leaf mold and soil. Irrespective of soil moisture condition, the overall accuracy tended to be stable at 92–94% with use of four VIS–NIR images. The spectral bands effective in wet soil conditions could also be used for classification in dry conditions, with overall accuracies >86.9%. These results suggest that automatic image analysis using VIS–NIR images at four spectral bands, including red and NIR, allows for accurate classification of the growth stage or live/dead status of roots and distinguishes between leaf mold and soil.     

Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (R_P = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.     

Edge disturbance shapes liana diversity and abundance but not liana‐tree interaction network patterns in moist semi‐deciduous forests,Ghana

Edge disturbance can drive liana community changes and alter liana‐tree interaction networks, with ramifications for forest functioning. Understanding edge effects on liana community structure and liana‐tree interactions is therefore essential for forest management and conservation. We evaluated the response patterns of liana community structure and liana‐tree interaction structure to forest edge in two moist semi‐deciduous forests in Ghana (Asenanyo and Suhuma Forest Reserves: AFR and SFR, respectively). Liana community structure and liana‐tree interactions were assessed in 24 50 × 50 m randomly located plots in three forest sites (edge, interior and deep‐interior) established at 0–50 m, 200 m and 400 m from edge. Edge effects positively and negatively influenced liana diversity in forest edges of AFR and SFR, respectively. There was a positive influence of edge disturbance on liana abundance in both forests. We observed anti‐nested structure in all the liana‐tree networks in AFR, while no nestedness was observed in the networks in SFR. The networks in both forests were less connected, and thus more modular and specialised than their null models. Many liana and tree species were specialised, with specialisation tending to be symmetrical. The plant species played different roles in relation to modularity. Most of the species acted as peripherals (specialists), with only a few species having structural importance to the networks. The latter species group consisted of connectors (generalists) and hubs (highly connected generalists). Some of the species showed consistency in their roles across the sites, while the roles of other species changed. Generally, liana species co‐occurred randomly on tree species in all the forest sites, except edge site in AFR where lianas showed positive co‐occurrence. Our findings deepen our understanding of the response of liana communities and liana‐tree interactions to forest edge disturbance, which are useful for managing forest edge.     

Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐species Eucalyptus forests

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.     

Identification of seed sources and parents of Pinus sylvestris L. using visible–near infrared reflectance spectra and multivariate analysis

The potential of visible (VIS) and near infrared (NIR) spectroscopy for identifying seed sources and parents of Pinus sylvestris L. was studied. Seeds of a single family (clones AC1005 × BD1178) collected from three localities in Sweden—Sävar (north), Röskär (central) and Degeberga (south)—and seeds from four maternal (clone no. BD1032, AC1014, BD1178 and AC1005) and four paternal (Y3020, BD1178, AC1014 and BD1032) parents were used to evaluate the method. VIS and NIR reflectance spectra were recorded on individual seeds using a NIRSystems Model 6500 spectrometer from 400 to 2,498 nm with a resolution of 2 nm. The VIS + NIR spectroscopic data were pre-treated with multiplicative signal correction, and analysed by soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA). The computed models were later applied to classify samples in the external test sets. The results show that seed sources were identified with 100% classification accuracy using PLS-DA models in the VIS + NIR, VIS and NIR regions. The average classification accuracy for maternal parents ranged from 92% to 96%, while paternal parents were identified with 91.2–96% accuracies. The classification accuracy using the SIMCA approach was relatively low for seed sources as well as maternal and paternal parents. It can be concluded that VIS + NIR spectroscopy could be employed as a rapid and non-destructive method for monitoring putative seed sources. The result underscores the prospect of the technique for characterizing seeds based on genotype, thereby serving as a tool in tree improvement and breeding.     

Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

Robust models for predicting soil salinity that use visible and near-infrared (vis–NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis–NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future.     

Fire alters diversity,composition, and structure of dry tropical forests in the Eastern Ghats

Fire is known to have dramatic consequences on forest ecosystems around the world and on the livelihoods of forest‐dependent people. While the Eastern Ghats of India have high abundances of fire‐prone dry tropical forests, little is known about how fire influences the diversity, composition, and structure of these communities. Our study aimed to fill this knowledge gap by examining the effects of the presence and the absence of recent fire on tropical dry forest communities within the Kadiri watershed, Eastern Ghats. We sampled plots with and without evidence of recent fire in the Eswaramala Reserve Forest in 2008 and 2018. Our results indicate that even though stem density increases in the recently burned areas, species richness is lower because communities become dominated by a few species with fire resistance and tolerance traits, such as thick bark and clonal sprouting. Further, in the presence of fire, the size structure of these fire‐tolerant species shifts toward smaller‐sized, resprouting individuals. Our results demonstrate that conservation actions are needed to prevent further degradation of forests in this region and the ecosystem services they provide.     

Diversity and habitat association of medium and large mammals in Gibe Sheleko National Park,Southern Ethiopia

Complete documentation on the status of mammals is indispensable for appropriate conservation measures in protected areas. However, there is inadequate information on mammalian resources in the ecosystem of Gibe Sheleko National Park (GSNP). Thus, the study aimed to assess species diversity, abundance, and habitat association of medium‐ and large‐sized mammals in GSNP. We stratified the study area into five dominant habitat types, namely dense forest, wooded grassland, grassland, riverine forest, and farmland habitat types based on land cover and vegetation structures and further employed stratified random sampling technique across each habitat type. The sample transects covered 20% of the study area. Transect width ranged from 50 m to 400 m based on vegetation cover and visibility of mammals. The main data were collected via direct observation. Data were analyzed via chi‐square test and species diversity indexes. We recorded the total of 20 mammals species'' those belong to 10 families of which 8 species were large‐sized and 12 species medium‐sized mammals. There were two IUCN vulnerable species, namely Hippopotamus amphibious and Panthera pardus, and two globally near‐threatened species, particularly Litocranius walleri and Caracal caracal in the study area. Dense forest held the highest species diversity of medium‐ and large‐sized mammals (H′ = 2.28) with the highest evenness index (J = 0.84). Riverine forest had the least diversity with uneven population distribution. Papio anubis was the most abundance species, whereas Caracal caracal was the least abundant in the study area. GSNP is home for threatened and spectacular mammals species''; hence, an appropriate conservation measure is mandatory to keep existing mammals species''.     

Transcriptional response of mushrooms to artificial sun exposure

Climate change causes increased tree mortality leading to canopy loss and thus sun‐exposed forest floors. Sun exposure creates extreme temperatures and radiation, with potentially more drastic effects on forest organisms than the current increase in mean temperature. Such conditions might potentially negatively affect the maturation of mushrooms of forest fungi. A failure of reaching maturation would mean no sexual spore release and, thus, entail a loss of genetic diversity. However, we currently have a limited understanding of the quality and quantity of mushroom‐specific molecular responses caused by sun exposure. Thus, to understand the short‐term responses toward enhanced sun exposure, we exposed mushrooms of the wood‐inhabiting forest species Lentinula edodes, while still attached to their mycelium and substrate, to artificial solar light (ca. 30°C and 100,000 lux) for 5, 30, and 60 min. We found significant differentially expressed genes at 30 and 60 min. Eukaryotic Orthologous Groups (KOG) class enrichment pointed to defense mechanisms. The 20 most significant differentially expressed genes showed the expression of heat‐shock proteins, an important family of proteins under heat stress. Although preliminary, our results suggest mushroom‐specific molecular responses to tolerate enhanced sun exposure as expected under climate change. Whether mushroom‐specific molecular responses are able to maintain fungal fitness under opening forest canopies remains to be tested.     

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.     

Seed dispersal by carnivores in temperate and tropical dry forests

The seed dispersal mechanisms and regeneration of various forest ecosystems can benefit from the actions of carnivores via endozoochory. This study was aimed to evaluate the role of carnivores in endozoochory and diploendozoochory, as well as their effect on seed viability, scarification, and germination in two forest ecosystems: temperate and tropical dry forest. We collected carnivore scat in the Protected Natural Area of Sierra Fría in Aguascalientes, Mexico, for 2 years to determine the abundance and richness of seeds dispersed by each carnivore species, through scat analysis. We assessed seed viability through optical densitometry using X‐rays, analyzed seed scarification by measuring seed coat thickness using a scanning electron microscope, and evaluated seed germination in an experiment as the percentage of seeds germinated per carnivore disperser, plant species, and forest type. In the temperate forest, four plant species (but mainly Arctostaphylos pungens) were dispersed by four mammal species. The gray fox dispersed the highest average number of seeds per scat (66.8 seeds). Bobcat dispersed seeds through diploendozoochory, which was inferred from rabbit (Sylvilagus floridanus) hair detected in their scats. The tropical dry forest presented higher abundance of seeds and richness of dispersed plant species (four species) than in the temperate forest, and the coati dispersed the highest number of seeds (8,639 seeds). Endozoochory and diploendozoochory did not affect viability in thick‐testa seeds (1,480 µm) in temperate forest and thin‐testa seeds (281 µm) in tropical dry forest. Endozoochory improved the selective germination of seeds. Nine plant species were dispersed by endozoochory, but only one species (Juniperus sp.) by diploendozoochory. These results suggest that carnivores can perform an important ecological function by dispersing a great abundance of seeds, scarifying these seeds causing the formation of holes and cracks in the testas without affecting viability, and promoting the selective germination of seeds.     

Pattern of crop raiding by wild large mammals and the resultant impacts vary with distances from forests in Southwest Ethiopia

Crop raiding is a major form of human‐wildlife interaction mainly in the ecotone areas of human‐modified natural landscapes. The aim of this study was to examine the spatial pattern of crop raiding and the resultant impacts on how farmers perceive forests at different distances from Yayu Coffee Forest Biosphere Reserve which is located in southwest Ethiopia. For this, thirty transects (each 1 km long) were laid out at 200 m interval parallel to forest edges: ten transects close to forest (<0.5 km), ten at intermediate (0.5–1 km), and ten transects were taken far from forest (>1 km). Along each transect, 2–6 households were randomly selected and interviewed using semistructured questionnaire. The perception of the respondents on forests at different distances from forest edges was analyzed using Pearson''s Chi‐square test. The variation in the amount of damage among these three locations was tested using one‐way ANOVA. Four wild large mammals including olive baboon, vervet monkey, bush pigs, and crested porcupine were identified as top crop raiders in the area. The frequencies of occurrence of crop raiders decreased with increasing distance from forest edges. Similarly, the amount of damage in maize fields was higher close to forests when compared with that of either at intermediate or far from forest edges (p < .001). Eighty‐one percent of the households living close to the forests perceive that forest is a threat to their survival. Overall, our results imply that strategies need to be sought in order to minimize the socio‐ecological impacts of crop raiders mainly in locations close to forest edges.     

Spectral changes with leaf aging in Amazon caatinga

 Significant gaps exist in the knowledge of tropical leaf spectra and the manner in which spectra change as leaves age in their natural environment. Leaf aging effects may be particularly important in tropical vegetation growing on nutrient poor soils, such as Amazon caatinga, a white sand community common in the Amazon Basin. Spectral changes observed in six caatinga dominants include decreased reflectance and transmittance and increased absorptance for epiphyll-coated older leaves. Near-infrared (NIR) changes were most significant. More detailed spectral and physical changes were studied in one dominant, Aldina heterophylla. Over 16 months, Aldina study plants produced one or two leaf flushes. During leaf expansion, leaf water content and Specific Leaf Area decreased rapidly. Over the first 6 months spectral changes occurred across the spectrum, resulting in decreased transmittance and increased absorptance in the visible and NIR and decreased visible and increased NIR reflectance. In contrast, significant spectral changes were restricted to the NIR over the last 9 months, which showed a 10% absorptance increase associated primarily with increasing epiphylls and necrosis. At the canopy scale, increased NIR absorptance provides a mechanism for producing seasonally varying forest albedo and changing NIR to red ratios, independent of changes in other canopy attributes. In the Amazon caatinga studied, all canopy dominants were subject to epiphyllic growth providing a mechanism for distinguishing these forest types spectrally from more diverse terra-firme forest or forest types with more rapid leaf turnover, such as second growth. These changes are observable using remote sensing and could be used to map caatinga and monitor interannual or seasonal variability in phenology. If these results can be extended to other communities with long-lived foliage, they may offer a means for mapping vegetation on the basis of leaf longevity. Received: 18 November 1996/Accepted: 24 December 1997     

Hyperspectral discrimination of coral reef benthic communities in the western Caribbean

Determining a subset of wavelengths that best discriminates reef benthic habitats and their associated communities is essential for the development of remote sensing techniques to monitor them. This study measured spectral reflectance from 17 species of western Caribbean reef biota including coral, algae, seagrasses, and sediments, as well as healthy and diseased coral. It sought to extend the spectral library of reef-associated species found in the literature and to test the spectral discrimination of a hierarchy of habitats, community groups, and species. We compared results from hyperspectral reflectance and derivative datasets to those simulated for the three visible multispectral wavebands of the IKONOS sensor. The best discriminating subset of wavelengths was identified by multivariate stepwise selection procedure (discriminant function analysis). Best discrimination at all levels was obtained using the derivative dataset based on 6–15 non-contiguous wavebands depending on the level of the classification, followed by the hyperspectral reflectance dataset which was based on as few as 2–4 non-contiguous wavebands. IKONOS wavebands performed worst. The best discriminating subset of wavelengths in the three classification resolutions, and particularly those of the medium resolution, was in agreement with those identified by Hochberg and Atkinson (2003) and Hochberg et al. (2003) for reef communities worldwide. At all levels of classification, reflectance wavebands selected by the analysis were similar to those reported in recent studies carried out elsewhere, confirming their applicability in different biogeographical regions. However the greater accuracies achieved using the derivative datasets suggests that hyperspectral data is required for the most accurate classification of reef biotic systems.     

Effects of phytoplankton species composition on absorption spectra and modeled hyperspectral reflectance

Understanding the spectral characteristics of remotely-sensed reflectance by different phytoplankton species can assist in the development of algorithms to identify various algal groups using satellite ocean color remote sensing. One of the main challenges is to separate the effect of species composition on the reflectance spectrum from other factors such as pigment concentration and particle size structure. Measuring the absorption spectra of nine different cultured algae, and estimating the reflectance of the different species, provides a useful approach to study the effects of species composition on the bio-optical properties. The results show that the absorption spectra of different species exhibit different spectral characteristics and that species composition can significantly change the absorption characteristics at four main peaks (438, 536, 600 and 650 nm). A ‘distance angle index’ was used to compare different phytoplankton species. Results indicate that this index can be used to identify species from the absorption spectra, using a database of standard absorption spectra of known species as reference. By taking into account the role of species composition in the phytoplankton absorption model, the performance of the model can be improved by up to 5%. A reflectance-species model is developed to estimate the remotely-sensed reflectance from the absorption spectra, and the reflectance of different phytoplankton species at the same chlorophyll-a concentration is compared, to understand effects of species composition on the reflectance spectra. Different phytoplankton species can cause up to 33% difference in the modeled reflectance at short wavelengths under the condition of the same chlorophyll-a concentration, and variations in the reflectance spectrum correspond to the colors of the algae. The standard deviation of the reflectance among different species shows that the variations from 400 to 450 nm are sensitive to species composition at low chlorophyll-a concentrations, whereas variations in the 510 to 550 nm range are more sensitive under high chlorophyll-a concentrations. For this reason, the green bands may be more suitable for estimating species composition from hyperspectral satellite data during bloom conditions, whereas the blue bands may be more helpful in detection of species under low chlorophyll-a concentrations. In this theoretical approach, variations in reflectance at the same chlorophyll-a concentration can be used to identify phytoplankton species. Another approach to identify phytoplankton species from remotely-sensed hyperspectral reflectance measurements would be to derive the absorption spectra of phytoplankton from the reflectance measurements, and compare these with a standard database of absorption spectra.     

Natural seedling recruitment and regeneration in deforested and sand‐filled Mangrove forest at Eagle Island,Niger Delta,Nigeria

Seed recruitment is a major driver of mangrove restoration globally. It is hypothesized that soil condition and channel hydrology can accelerate seedling recruitment and regeneration after a major disturbance. Species abundance, diversity indices, microbial and chemical concentrations in sand‐filled mangrove forest was studied. Eight plots measuring 487.77 m² each were established with ten transects in each plot in a random block design to investigate the effect of soil conditions on seedling growth. A total of 1,886 seedlings were counted. Seedling abundance was significantly different between red (Rizophora racemosa), white (Laguncularia racemosa), and black (Avicennia germinans) mangroves, and nypa palm (nypa fruticans). The most dominant species was black mangrove, and the least dominant species was nypa palm. Muddy soils had the most abundant species (n = 994) followed by sandy (n = 457) and semi‐muddy (435) soils. Furthermore, sandy soils had the highest species diversity (H = 0.896) followed by semi‐muddy (H = 0.876) and muddy (H = 0.583) soils. The soil metal concentration has no correlation with seed abundance and occur in the order Iron > Nitrate > Copper > Cadmium. Soil with high species diversity had high soil microbial population; however, seedling abundance was correlated with soil nutrients and not heavy metals. Small seeds are easily recruited while good soil condition plus existing hydrological connection facilitated natural seedling regeneration in the disturbed mangrove forest.     

Spatial patterns of light‐demanding tree species in the Yangambi rainforest (Democratic Republic of Congo)

Most Central African rainforests are characterized by a remarkable abundance of light‐demanding canopy species: long‐lived pioneers (LLP) and non‐pioneer light demanders (NPLD). A popular explanation is that these forests are still recovering from intense slash‐and‐burn farming activities, which abruptly ended in the 19th century. This “human disturbance” hypothesis has never been tested against spatial distribution patterns of these light demanders. Here, we focus on the 28 most abundant LLP and NPLD from 250 one‐ha plots distributed along eight parallel transects (~50 km) in the Yangambi forest. Four species of short‐lived pioneers (SLP) and a single abundant shade‐tolerant species (Gilbertiodendron dewevrei) were used as reference because they are known to be strongly aggregated in recently disturbed patches (SLP) or along watercourses (G. dewevrei). Results show that SLP species are strongly aggregated with clear spatial autocorrelation of their diameter. This confirms that they colonized the patch following a one‐time disturbance event. In contrast, LLP and NPLD species have random or weakly aggregated distribution, mostly without spatial autocorrelation of their diameter. This does not unambiguously confirm the “human disturbance” hypothesis. Alternatively, their abundance might be explained by their deciduousness, which gave them a competitive advantage during long‐term drying of the late Holocene. Additionally, a canonical correspondence analysis showed that the observed LLP and NPLD distributions are not explained by environmental variables, strongly contrasting with the results for the reference species G. dewevrei, which is clearly aggregated along watercourses. We conclude that the abundance of LLP and NPLD species in Yangambi cannot be unambiguously attributed to past human disturbances or environmental variables. An alternative explanation is that present‐day forest composition is a result of adaptation to late‐Holocene drying. However, results are inconclusive and additional data are needed to confirm this alternative hypothesis.     

Common spatial patterns of trees in various tropical forests: Small trees are associated with increased diversity at small spatial scales

Tropical forests are notable for their high species diversity, even on small spatial scales, and right‐skewed species and size abundance distributions. The role of individual species as drivers of the spatial organization of diversity in these forests has been explained by several hypotheses and processes, for example, stochastic dilution, negative density dependence, or gap dynamics. These processes leave a signature in spatial distribution of small trees, particularly in the vicinity of large trees, likely having stronger effects on their neighbors. We are exploring species diversity patterns within the framework of various diversity‐generating hypotheses using individual species–area relationships. We used the data from three tropical forest plots (Wanang—Papua New Guinea, Barro Colorado Island—Panama, and Sinharaja—Sri Lanka) and included also the saplings (DBH ≥ 1 cm). Resulting cross‐size patterns of species richness and evenness reflect the dynamics of saplings affected by the distribution of large trees. When all individuals with DBH ≥1 cm are included, ~50% of all tree species from the 25‐ or 50‐ha plot can be found within 35 m radius of an individual tree. For all trees, 72%–78% of species were identified as species richness accumulators, having more species present in their surroundings than expected by null models. This pattern was driven by small trees as the analysis of DBH >10 cm trees showed much lower proportion of accumulators, 14%–65% of species identified as richness repellers and had low richness of surrounding small trees. Only 11%–26% of species had lower species evenness than was expected by null models. High proportions of species richness accumulators were probably due to gap dynamics and support Janzen–Connell hypothesis driven by competition or top‐down control by pathogens and herbivores. Observed species diversity patterns show the importance of including small tree size classes in analyses of the spatial organization of diversity.     

Airborne hyperspectral imaging for estimating acorn yield based on the PLS B-matrix calibration technique

Alternate bearing of acorn is a well-marked yield variability phenomenon in forest production. In Japan, this phenomenon is also related to wildlife management (e.g. of animals such as wild pigs, that rely on acorn as their major feed source). Effective management of animals dependent on acorn will require accurate estimation of acorn yield at an early stage. In this paper, we proposed a way to estimate acorn yield from the canopy reflectance values of individual trees. Using an Airborne Imaging Spectrometer for Application (AISA) Eagle System, hyperspectral images in 72 visible and near-infrared wavelengths (407–898 nm) were acquired over an acorn forest in Japan 10 times over three consecutive years (2003–2005) during the early acorn growing season. The canopy spectral reflectance values for individual trees at each wavelength were extracted from the images, and important wavelengths were determined as estimating factors by the B-matrix technique based on partial least squares (PLS) analysis. Yield-estimating models were then developed by multiple linear regression (MLR). Three models obtained from images acquired on June 27 in 2003, July 13 in 2004 and June 21 in 2005 estimated acorn yield well in comparison with ground truth, indicating that the procedure has considerable potential. The study also demonstrated the B-matrix technique based on PLS analysis to be reliable and efficient in identifying important wavelengths for determining suitable estimating factors that best contribute to the estimation model.     

A multi‐data ensemble approach for predicting woodland type distribution: Oak woodland in Britain

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate‐related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross‐validated (50%:50% ‐ training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS‐weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak‐stand locations as a training dataset; the national forest inventory (NFI) “published regional reports” of oak woodland area; and an “NFI map” of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from “NFI survey” sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.