Tallo: A global tree allometry and crown architecture database.

This is the final version. Available from Wiley via the DOI in this record. Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.Natural Environment Research Council (NERC)Natural Environment Research Council (NERC); Ministry of Education, Youth and Sports of the Czech RepublicFAPEMIGUniversidad Nacional Autónoma de MéxicoUniversidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y TecnologíaSwedish Energy AgencyUKRIFederal Ministry of Education and ResearchNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Science FoundationNational Science FoundationInternational Foundation for ScienceP3FACDynAfForNanjing Forestry UniversityJiangsu Science and Technology Special ProjectHebei UniversityAgence Nationale de la RechercheAgence Nationale de la RechercheAgua Salud ProjectU.S. Department of EnergyCAPE

Comparing the use of indigenous knowledge with classification and ordination techniques for assessing the species composition and structure of vegetation in a tropical forest

dentification of groups that are similar in their floristic composition and structure (habitat types) is essential for conservation and forest managers to allocate high priority areas and to designate areas for reserves, refuges, and other protected areas. In this study, the use of indigenous knowledge for the identification of habitat types in the field was compared against an ecological characterization of habitat types, including their species composition obtained by using classification and ordination techniques for a tropical landscape mosaic in a rural Mayan area of Quintana Roo, Mexico. Plant diversity data calculated from 141 sampled sites chosen randomly on a vegetation class's thematic map obtained by multispectral satellite image classification were used for this propose. Results indicated high similarity in the categorization of vegetation types between the Mayan classification and those obtained by cluster and detrended correspondence analysis. This suggests that indigenous knowledge has a practical use and can be comparable to that obtained by using science-based methods. Finally, identification and mapping of vegetation classes (habitat types) using satellite image classification allowed us to discriminate significantly different species compositions, in such a way that they can provide a useful mechanism for interpolating diversity values over the entire landscape

Partitioning the variation of woody plant ?-diversity in a landscape of secondary tropical dry forests across spatial scales

What is the relative importance of forest successional age, environmental heterogeneity, landscape structure and spatial structure of sampling sites on ?-diversity of tropical dry forests (TDF)? How do the magnitude of ?-diversity and the relative influence of factors, processes and mechanisms driving ?-diversity differ at different spatial grains? What are the effects of stand age on ?-diversity

Scale dependency of the effects of landscape structure and stand age on species richness and aboveground biomass of tropical dry forests

The structure and diversity of plant communities respond to changes in landscape structure and vary with spatial scale, stand age and plant size. Therefore, it is important to identify the scale (grain size and extent) at which secondary forest attributes of large and/or small plants and landscape structure are more closely associated. We performed multi-scale analyses in which different grain sizes and extents were assessed to determine the most appropriate spatial scale for assessing the association of large/small tree aboveground biomass and species richness with successional age and landscape structure using regression analysis. AGB and species richness were more strongly associated with landscape structure when large grain sizes (500 m2) were used, with R2 values between 0.31 and 0.43. Variation in AGB and species richness was explained primarily by successional age and landscape structure, respectively. At large extents, successional age was related to the AGB of large trees (R2 = 0.43); at intermediate extents, landscape structure was related to the species richness of large trees (R2 = 0.31). The approach and results of this study may facilitate the identification of appropriate areas and scales for the maintenance or restoration of tree diversity, carbon storage, and the provision of ecosystem services in tropical dry forests

Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping

Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R2 values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R2 values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests

Spatial structure of plant communities in a complex tropical landscape: implications for β-diversity

The importance of space as an ecological factor is an emerging paradigm in community ecology, particularly as a driving force of biodiversity patterns. We analysed β-diversity linked to spatial structure in four communities (tropical dry forest, savanna, xerophytic vegetation, subdeciduous forest) that occur in a tropical complex landscape of southern Mexico. The landscape was described through an object-oriented classification of a Quickbird satellite image. The classification revealed a highly heterogeneous spatial arrangement of the four communities. Global (landscape-level) β-diversity was 0.12 (mean Sørensen index), a value smaller than those observed for the individual communities (0.20–0.41). By using multivariate classic and partial Mantel tests, and Mantel correlograms based on two distance classes, we analysed β-diversity spatial variation related to landscape configuration. The Mantel statistic values for the four communities combined were negative and very similar both for the classic Mantel (rM = −0.23) and the partial Mantel test (rM = −0.19). Correlograms proved significant spatial autocorrelation across most of the analysed distance classes, except in the case of riparian subdeciduous forest. Tropical dry forest and savanna occupy large, highly connected areas in the landscape. Correlograms for these two communities showed decreasing trends, starting at positive, significant autocorrelation values at short distances. The loss of floristic autocorrelation beyond a 5000 m distance for the tropical dry forest may reflect spatially autocorrelated dispersal, and thus a dispersal-limited community. Xerophytic vegetation displayed a distinctly different correlogram, with a wavy shape showing an alternation of positive and negative values, in agreement with the insular configuration of this community in the landscape. We suggest that the routinely incorporation of a spatial approach in community ecology research will help in furthering our understanding of the elusive issues of species turnover across space

Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (?- and ?-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining ?- and ?-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of ?- and ?-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map ?- and ?-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (?-diversity), but also areas with high species turnover (?-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape

Can tree species diversity be assessed with Landsat data in a temperate forest?

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gonen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m x 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 x 2 pixels and 3 x 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015