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TRY plant trait database – enhanced coverage and open access
Kattge , Jens , Bönisch , Gerhard , Díaz , Sandra M. , Lavorel , Sandra , Prentice , Iain Colin , Leadley , Paul W. , Tautenhahn , Susanne , Werner , Gijsbert , Aakala , Tuomas , Abedi , Mehdi , Acosta , Alicia Teresa Rosario , Adamidis , George C. , Adamson , Kairi , Aiba , Masahiro , Albert , Cécile Hélène , Alcántara , Julio M. , Alcázar C , Carolina , Aleixo , Izabela , Ali , Hamada E. , Amiaud , Bernard , Ammer , Christian , Amoroso , Mariano Martín , Anand , Madhur , Anderson , Carolyn G. , Anten , Niels P.R. , Antos , Joseph A. , Apgaua , Deborah Mattos Guimarães , Ashman , Tia Lynn , Asmara , Degi Harja , Asner , Gregory P. , Aspinwall , Michael J. , Atkin , Owen K. , Aubin , Isabelle , Baastrup-Spohr , Lars , Bahalkeh , Khadijeh , Bahn , Michael , Baker , Timothy R. , Baker , William J. , Bakker , Jan P. , Baldocchi , Dennis D. , Baltzer , Jennifer L. , Banerjee , Arindam , Baranger , Anne , Barlow , Jos B. , Barneche , Diego R. , Baruch , Zdravko , Bastianelli , Denis , Battles , John J. , Bauerle , William L. , Bauters , Marijn , Bazzato , Erika , Beckmann , Michael , Beeckman , Hans , Beierkuhnlein , Carl , Bekker , Renée M. , Belfry , Gavin , Belluau , Michaël , Beloiu Schwenke , Mirela , Benavides , Raquel , Benomar , Lahcen , Berdugo-Lattke , Mary Lee , Berenguer , Erika , Bergamin , Rodrigo Scarton , Bergmann , Joana , Carlucci , Marcos B. , Berner , Logan T. , Bernhardt-Römermann , Markus , Bigler , Christof , Bjorkman , Anne D. , Blackman , Chris J. , Blanco , Carolina Casagrande , Blonder , Benjamin Wong , Blumenthal , Dana M. , Bocanegra-González , Kelly Tatiana , Boeckx , Pascal , Bohlman , Stephanie Ann , Böhning-Gaese , Katrin , Boisvert-Marsh , Laura , Bond , William J. , Bond-Lamberty , Ben P. , Boom , Arnoud , Boonman , Coline C.F. , Bordin , Kauane Maiara , Boughton , Elizabeth H. , Boukili , Vanessa K.S. , Bowman , David M.J.S. , Bravo , Sandra Josefina , Brendel , Marco R. , Broadley , Martin R. , Brown , Kerry A. , Bruelheide , Helge , Brumnich , Federico , Bruun , Hans Henrik , Bruy , David , Buchanan , Serra Willow , Bucher , Solveig Franziska , Buchmann , Nina , Buitenwerf , Robert , Bunker , Daniel E. , Bürger , Jana
functional diversity data coverage data integration data representativeness plant traits try plant trait database
Mostra abstract
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. © 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd
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2020
Rivista
Global Change Biology
Dettagli
Vol. 26 (1) pp. 119 - 188
DOI
Comparison of seven inversion models for estimating plant andwoody area indices of leaf-on and leaf-off forest canopy using explicit 3D forest scenes
Zou , Jie , Zhuang , Yinguo , Chianucci , Francesco , Mai , Chunna , Lin , Weimu , Leng , Peng , Luo , Shezhou , Yan , Bojie
forest canopy canopy element and woody component projection functions clumping effect digital hemispherical photography forest scenes inversion model leaf area index (lai) plant area index (pai) woody area index (wai)
Mostra abstract
Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI,WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI andWAI of the leaf-on and leaf-off forest canopy. The PAI andWAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI,WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level ( < 4%) by adopting appropriate inversion models. © 2018 by the authors.
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2018
Rivista
Remote Sensing
Dettagli
Vol. 10 (8)
DOI