Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics

Question: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species? Location: Quintana Roo, Yucatan peninsula, Mexico. Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model‐averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation.     

Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing

Aim Conservation activities have increasingly focused on issues at the level of the landscape but are constrained by limited data and knowledge relating to biodiversity at this scale. Satellite remote sensing has considerable, but under‐exploited, potential as a source of information on biodiversity at the landscape level. Remote sensing has generally been used to assess biodiversity indirectly, using approaches that often fail to fully exploit the information content of the imagery and typically only with regard to the species richness component of biodiversity. The aim of this paper was to assess the potential of remote sensing as a source of information on the richness, evenness and composition of tree species in a tropical rain forest. Location The test site was a c. 225 km² region centred on the Danum Valley Field Centre, Borneo. This test site contained regions of undisturbed and differentially logged rain forest. Methods Data on tree biodiversity had been acquired for fifty‐two sample plots by standard field survey methods and were used to derive summary indices of biodiversity for seedlings, saplings and mature trees. Differences between logged and unlogged sites were evaluated by comparison of the indices and species accumulation curves. A Landsat Thematic Mapper (TM) image of the site acquired close to the date of the field survey was obtained and rigorously pre‐processed. Feedforward neural networks were used to derive predictions of biodiversity indices from the imagery. A Kohonen self organizing map neural network was used to ordinate the field data to derive classes of forest defined by relative similarity in species composition. The separability of the defined classes in the Landsat TM image was evaluated with a discriminant analysis. Results Analyses of the field data revealed considerable variation in the biodiversity of seedlings, saplings and trees at the site, associated, in part, with differences in logging activities. This variation in biodiversity was manifest in the remotely sensed data. The analyses indicated an ability to (1) predict biodiversity indices, with the highest correlation between predicted and actual index observed for evenness described by Shannon entropy (r = 0.546), but especially to (2) classify nine forest classes defined on the basis of similarity in tree species composition (accuracy 95.8%). Main conclusions Logging activities impacted on biodiversity and the resulting variation in biodiversity was reflected in the remotely sensed imagery. Using methods that exploit more fully the information content of the imagery than those used in other previous studies, a richer representation of biodiversity may be derived. This representation includes estimates of key summary indices of biodiversity, notably richness and evenness, as well as information on species composition. The results indicate that remotely sensed data may be used as a source of information on biodiversity at the landscape scale that may be used to inform conservation science and management.     

基于辅助变量的森林半腐层厚度空间插值精度

基于地统计学方法,利用3种以海拔作为辅助变量的空间插值算法［局部平均的简单kriging法(simple kriging with locally varying mean,SKlm)、带有外部漂移的kriging法(kriging with an external drift,KED)和协kriging法(cokrging,COK)］计算了森林半腐层厚度的空间插值精度,并进行了交叉验证.结果表明：KED法既考虑了变量之间的空间变异,又考虑到影响局部空间变化的因素,与其他插值方法相比,其精度有很大提高;由于海拔与半腐层厚度之间的相关关系较弱,导致SKlm法的插值精度没有达到预期效果;COK法直接将海拔用于估计半腐层厚度,由于在边界地区缺乏采样点数据,因此边界地区的插值出现了多处突变区域.对比地统计学方法与距离反比权重法(inverse distance weighting, IDW)在本研究中的插值精度,除了KED方法的插值精度较高外,其余方法的插值精度均不及IDW,原因可能是利用辅助变量辅助地统计学插值时,主、辅助变量之间的相关关系在插值中起着重要作用.     

Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems

Assessing biodiversity in arctic-alpine ecosystems is a costly task. We test in the current study whether we can map the spatial patterns of spider alpha and beta diversity using remotely-sensed surface reflectance and topography in a heterogeneous alpine environment in Central Norway. This proof-of-concept study may provide a tool for an assessment of arthropod communities in remote study areas. Data on arthropod species distribution and richness were collected through pitfall trapping and subjected to a detrended correspondence analysis (DCA) to extract the main species composition gradients. The DCA axis scores as indicators of species composition as well as trap species richness were regressed against a combined data set of surface reflectance as measured by the Sentinel-2 satellite and topographical parameters extracted from a digital elevation model. The models were subsequently applied to the spatial data set to achieve a pixel-wise prediction of both species richness and position in the DCA space. The spatial variation in the modelled DCA scores was used to draw conclusions regarding spider beta-diversity. The species composition was described with two DCA axes that were characterized by post hoc-defined indicator species, which showed a typical annidation in the arctic-alpine environment under study. The fits of the regression models for the DCA axes and species richness ranged from R² = 0.25 up to R² = 0.62. The resulting maps show strong gradients in alpha and beta diversity across the study area. Our results indicate that the diversity patterns of spiders can at least partially be explained by means of remotely sensed data. Our approach would likely benefit from the additional use of high resolution aerial photography and LiDAR data and may help to improve conservation strategies in arctic-alpine ecosystems.     

Using satellite imagery to assess plant species richness: The role of multispectral systems

Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness. Location: Montepulciano Lake, central Italy. Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis. Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R²∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers. Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.     

Forest disturbance and recovery in Peruvian Amazonia

Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha⁻¹ year⁻¹ during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.     

Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests

Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawai’i. Hyperspectral signatures spanning the 400–2,500 nm wavelength range acquired by the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were analyzed at 17 forest sites with species richness values ranging from 1 to 17 species per 0.1–0.3 ha. Spatial variation (range) in the shape of the AVIRIS spectra (derivative reflectance) in wavelength regions associated with upper-canopy pigments, water, and nitrogen content were well correlated with species richness across field sites. An analysis of leaf chlorophyll, water, and nitrogen content within and across species suggested that increasing spectral diversity was linked to increasing species richness by way of increasing biochemical diversity. A linear regression analysis showed that species richness was predicted by a combination of four biochemically-distinct wavelength observations centered at 530, 720, 1,201, and 1,523 nm (r ² = 0.85, p < 0.01). This relationship was used to map species richness at approximately 0.1 ha resolution in lowland forest reserves throughout the study region. Future remote sensing studies of biodiversity will benefit from explicitly connecting chemical and physical properties of the organisms to remotely sensed data.     

Patterns of bird diversity and habitat use in mixed vineyard-matorral landscapes of Central Chile

The Mediterranean climate region of central Chile is rich in biodiversity and contains highly productive agricultural lands, which creates challenges for the preservation of natural habitats and native biodiversity. Ecological data and studies for the region are also limited, making informed conservation in agricultural landscapes difficult. The increasing availability of remotely sensed data provide opportunities to relate species occurrences to measures of landscape heterogeneity even when field measures of habitat structure are lacking. When working with such remotely sensed data, it’s important to select appropriate measures of heterogeneity, including common metrics of landscape composition as well as frequently overlooked shape metrics. In this contribution we combine bird surveys with multispectral satellite imagery to develop boosted regression tree models of avian species richness, and of habitat use for 15 species across a mixed vineyard-matorral landscape in central Chile. We found a range of associations between individual species and land cover types, with the majority of species occurring most frequently in remnant habitats and ecotones rather than the interiors of large vineyard blocks. Models identified both metrics of landscape composition and patch shape as being important predictors of species occurrence, suggesting that shape metrics can complement more commonly used metrics of landscape composition. Vineyards that include corridors or islands of remnant habitat among vine blocks may increase the amount of area available to many species, although some species may still require large tracts of intact natural habitat to persist.     

Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.     

Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?

Different approaches for the assessment of biodiversity by means of remote sensing were developed over the last decades. A new approach, based on the spectral variation hypothesis, proposes that the spectral heterogeneity of a remotely sensed image is correlated with landscape structure and complexity which also reflects habitat heterogeneity which itself is known to enhance species diversity. In this context, previous studies only applied species richness as a measure of diversity. The aim of this paper was to analyze the relationship of richness and abundance-based diversity measures with spectral variability and compare the results at two scales. At three different test sites in Central Namibia, measures of vascular plant diversity was sampled at two scales – 100 m² and 1000 m². Hyperspectral remote sensing data were collected for the study sites and spectral variability, was calculated at plot level. Ordinary least square regression was used to test the relationship between species richness and the abundance-based Shannon Index and spectral variability. We found that Shannon Index permanently achieved better results at all test sites especially at 1000 m², Even when all sites where pooled together, Shannon Index was still significantly related with spectral variability at 1000 m². We suggest incorporating abundance-based diversity measures in studies of relationships between ecological and spectral variability. The contribution made by the high spectral and spatial resolution of the hyperspectral sensor is discussed.     

Can we predict butterfly diversity along an elevation gradient from space?

An important challenge in ecology is to predict patterns of biodiversity across eco‐geographical gradients. This is particularly relevant in areas that are inaccessible, but are of high research and conservation value, such as mountains. Potentially, remotely‐sensed vegetation indices derived from satellite images can help in predicting species diversity in vast and remote areas via their relationship with two of the major factors that are known to affect biodiversity: productivity and spatial heterogeneity in productivity. Here, we examined whether the Normalized Difference Vegetation Index (NDVI) can be used effectively to predict changes in butterfly richness, range size rarity and beta diversity along an elevation gradient. We examined the relationship between butterfly diversity and both the mean NDVI within elevation belts (a surrogate of productivity) and the variability in NDVI within and among elevation belts (surrogates for spatial heterogeneity in productivity). We calculated NDVI at three spatial extents, using a high spatial resolution QuickBird satellite image. We obtained data on butterfly richness, rarity and beta diversity by field sampling 100 m quadrats and transects between 500 and 2200 m in Mt Hermon, Israel. We found that the variability in NDVI, as measured both within and among adjacent elevation belts, was strongly and significantly correlated with butterfly richness. Butterfly range size rarity was strongly correlated with the mean and the standard deviation of NDVI within belts. In our system it appears that it is spatial heterogeneity in productivity rather than productivity per se that explained butterfly richness. These results suggest that remotely‐sensed data can provide a useful tool for assessing spatial patterns of butterfly richness in inaccessible areas. The results further indicate the importance of considering spatial heterogeneity in productivity along elevation gradients, which has no lesser importance than productivity in shaping richness and rarity, especially at the local scale.     

Bird diversity: a predictable function of satellite-derived estimates of seasonal variation in canopy light absorbance across the United States

Aim  To investigate the relationships between bird species richness derived from the North American Breeding Bird Survey and estimates of the average, minimum, and the seasonal variation in canopy light absorbance (the fraction of absorbed photosynthetically active radiation, fPAR) derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS).
Location  Continental USA.
Methods  We describe and apply a 'dynamic habitat index' (DHI), which incorporates three components based on monthly measures of canopy light absorbance through the year. The three components are the annual sum, the minimum, and the seasonal variation in monthly fPAR, acquired at a spatial resolution of 1 km, over a 6-year period (2000–05). The capacity of these three DHI components to predict bird species richness across 84 defined ecoregions was assessed using regression models.
Results  Total bird species richness showed the highest correlation with the composite DHI [ R ² = 0.88, P  < 0.001, standard error of estimate (SE) = 8 species], followed by canopy nesters ( R ² = 0.79, P  < 0.001, SE = 3 species) and grassland species ( R ² = 0.74, P  < 0.001, SE = 1 species). Overall, the seasonal variation in fPAR, compared with the annual average fPAR, and its spatial variation across the landscape, were the components that accounted for most ( R ² = 0.55–0.88) of the observed variation in bird species richness.
Main conclusions  The strong relationship between the DHI and observed avian biodiversity suggests that seasonal and interannual variation in remotely sensed fPAR can provide an effective tool for predicting patterns of avian species richness at regional and broader scales, across the conterminous USA.     

Drivers of Echinococcus multilocularis Transmission in China: Small Mammal Diversity,Landscape or Climate?

Background

Human alveolar echinococcocosis (AE) is a highly pathogenic zoonotic disease caused by the larval stage of the cestode E. multilocularis. Its life-cycle includes more than 40 species of small mammal intermediate hosts. Therefore, host biodiversity losses could be expected to alter transmission. Climate may also have possible impacts on E. multilocularis egg survival. We examined the distribution of human AE across two spatial scales, (i) for continental China and (ii) over the eastern edge of the Tibetan plateau. We tested the hypotheses that human disease distribution can be explained by either the biodiversity of small mammal intermediate host species, or by environmental factors such as climate or landscape characteristics.

Methodology/findings

The distributions of 274 small mammal species were mapped to 967 point locations on a grid covering continental China. Land cover, elevation, monthly rainfall and temperature were mapped using remotely sensed imagery and compared to the distribution of human AE disease at continental scale and over the eastern Tibetan plateau. Infection status of 17,589 people screened by abdominal ultrasound in 2002–2008 in 94 villages of Tibetan areas of western Sichuan and Qinghai provinces was analyzed using generalized additive mixed models and related to epidemiological and environmental covariates. We found that human AE was not directly correlated with small mammal reservoir host species richness, but rather was spatially correlated with landscape features and climate which could confirm and predict human disease hotspots over a 200,000 km² region.

Conclusions/Significance

E. multilocularis transmission and resultant human disease risk was better predicted from landscape features that could support increases of small mammal host species prone to population outbreaks, rather than host species richness. We anticipate that our study may be a starting point for further research wherein landscape management could be used to predict human disease risk and for controlling this zoonotic helminthic.     

Environmental correlates of plant diversity in Korean temperate forests

Mountainous areas of the Korean Peninsula are among the biodiversity hotspots of the world's temperate forests. Understanding patterns in spatial distribution of their species richness requires explicit consideration of different environmental drivers and their effects on functionally differing components. In this study, we assess the impact of both geographical and soil variables on the fine-scale (400 m²) pattern of plant diversity using field data from six national parks, spanning a 1300 m altitudinal gradient. Species richness and the slopes of species–area curves were calculated separately for the tree, shrub and herb layer and used as response variables in regression tree analyses. A cluster analysis distinguished three dominant forest communities with specific patterns in the diversity–environment relationship. The most widespread middle-altitude oak forests had the highest tree richness but the lowest richness of herbaceous plants due to a dense bamboo understory. Total richness was positively associated with soil reaction and negatively associated with soluble phosphorus and solar radiation (site dryness). Tree richness was associated mainly with soil factors, although trees are frequently assumed to be controlled mainly by factors with large-scale impact. A U-shaped relationship was found between herbaceous plant richness and altitude, caused by a distribution pattern of dwarf bamboo in understory. No correlation between the degree of canopy openness and herb layer richness was detected. Slopes of the species–area curves indicated the various origins of forest communities. Variable diversity–environment responses in different layers and communities reinforce the necessity of context-dependent differentiation for the assessment of impacts of climate and land-use changes in these diverse but intensively exploited regions.     

A cloud-based toolbox for the versatile environmental annotation of biodiversity data

A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity–environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.

In ecology and evolution, processes, data collection, and inference or prediction usually occur at different scales in space and time. This study introduces a cloud-based toolbox for the flexible fusion of biodiversity records with remotely sensed and other environmental information that supports an assessment and accounting of such scale dependencies.     

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

Predicting broad-scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad-scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine-scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e., surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty-two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first-order and second-order image textures measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave-one-out cross-validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi-arid ecosystems and that image texture is a promising tool when assessing broad-scale patterns of biodiversity using remotely sensed data.     

Land use and ostracod community structure

Ostracods are important members of the benthos and littoral communities of lake ecosystems. Ostracods respond to hydrochemistry (water chemistry) which is influenced by climatic factors such as water balance, temperature, and chemicals in rainfall runoff from the land. Thus, at local scales, environmental preferences of ostracods and characteristics of lakes are used to infer changes in climate, hydrology, and erosion of lake catchments. This study addresses potential drivers of ostracod community structure and biodiversity at multiple spatial scales using NMS, CART^?, and multiple regression models. We identified 23 ostracod species from 12 lake sites. Lake area, maximum depth, spring conductivity, chlorophyll a, pH, dissolved oxygen, sedimentary carbonate, and organic matter all influence ostracod community structure based on our NMS. Based on regression analysis, lake depth, chlorophyll a, and total dissolved solids best explained ostracod richness and abundance. Land uses are also important community structuring elements that varied with scale; locally and regionally agriculture, wetlands, and grasslands were important. Nationally, using regression tree analysis of lakes sites in the North American Non-marine ostracod database (NANODe), row-crop agriculture was the most important predictor of biodiversity. Low agriculture corresponded to low species richness but greater landscape heterogeneity produced sites of high ostracod richness.     

Species richness of woody plants in the landscapes of Central Spain: the role of management disturbances,environment and non‐stationarity

Questions: How important is management disturbance on gamma species richness of woody plants at intermediate landscape scales? How is species richness related to other climatic and biotic factors in the study area? How does the assumption of spatial stationarity affect assessment of relationships among species richness and explanatory variables (e.g. management, biotic and climatic factors) across extensive study areas? Location: Central Spain (regions of Castilla y León, Madrid and Castilla‐La Mancha). Scale: Extent: 150 000 km². Grain: 25 km² (5 × 5‐km cells). Methods: Information from 21 064 plots from the 3SNFI was used to evaluate richness of tree and shrub species at intermediate landscape scales. In addition to variables well known to explain biodiversity, e.g. environmental and biotic factors, effect of management treatments was evaluated by assessing clearcutting, selection cutting, stand improvement treatments and agrosilvopastoral systems (dehesas). Results from GWR techniques were compared with those from OLS regression. Results: Patterns of gamma species richness, although strongly affected by both environmental and biotic variables, were also significantly modified by management factors. Species richness increased with percentage of selection cutting stands and improvement treatments but decreased with percentage of clearcutting stands. Reduced species richness of woody plants was associated with agrosilvopastoral practices. Species richness for trees was closely related to basal area, annual precipitation and topographic complexity; species richness for shrubs was closely related to topographic complexity and agrosilvopastoral systems. Most relationships between species richness and environmental or biotic factors were non‐stationary. Relationships between species richness and management effects tended to be stationary, with a few exceptions. Conclusions: Landscape models of biodiversity in Central Spain were more informative when they accounted for effects of management practices, at least at intermediate scales. In the context of current rural abandonment, silvicultural disturbances of intermediate intensity increased gamma species richness of woody plants. Exclusion of factors such as agrosilvopastoral systems from models could have led to spurious relationships with other spatially co‐varying factors (e.g. summer precipitation). Patterns of spatial variation in relationships, provided by GWR models, allowed formulating hypotheses about potential ecological processes underlying them, beyond generalizations resulting from global (OLS) models.     

Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data

With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data.Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed.According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species; r² = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest.In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be applied also for estimating beta diversity.     

Modelled photosynthesis predicts woody plant richness at three geographic scales across the north-western United States

Aim We analyse regional patterns of woody plant species richness collected from field data in relation to modelled gross photosynthesis, P_g, compare the performance of P_g in relation to other productivity surrogates, and examine the effect of increasing scale on the productivity–richness relationship. Location The forested areas in the north‐western states of Oregon, Washington, Idaho, and Montana, USA. Methods Data on shrub and tree species richness were assembled from federal vegetation surveys and compared with modelled growing season gross photosynthesis, P_g (the sum of above‐ and below‐ground production plus autotrophic respiration) and two measures of spatial heterogeneity. We analysed the productivity–richness relationship at different scales by changing the focus size through spatial aggregation of field plots using 100 and 1000 km² windows covering the study area. Regression residuals were plotted spatially. Using the best available tree data set (Continuous Vegetation Survey: CVS), we compared different productivity indices, such as actual evapotranspiration and average temperature, in their ability to predict patterns of tree species richness. Results The highest species richness (species/unit area) occurred at intermediate levels of productivity. After accounting for variable sampling intensity, the richness–productivity relationship improved as more field plots were aggregated. At coarser levels of aggregation, modelled productivity accounted for 57–71% of the variation in richness patterns for shrubs and trees (CVS data set). Measures of spatial heterogeneity accounted for more variation in richness patterns aggregated by 100 km² windows than aggregation by 1000 km² windows. P_g was a better predictor of tree richness in Oregon and Washington (CVS data set) than any surrogate productivity index. Main conclusions P_g was observed to be a strong unimodal predictor of both tree (CVS) and shrub (FIA) richness when field data were aggregated. For the tree data set examined, seasonally integrated estimates of photosynthesis (P_g) predicted tree richness patterns better than climatic indices did.