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Evolutionary diversity is associated with wood productivity in Amazonian forests

December 2019
Nature Ecology & Evolution 3(12):1-8

DOI:10.1038/s41559-019-1007-y

Authors:

Fernanda Coelho

University of Leeds

Kyle G Dexter

The University of Edinburgh

Oliver Lawrence Phillips

University of Leeds

Show all 75 authorsHide

Higher levels of taxonomic and evolutionary diversity are expected to maximize ecosystem function, yet their relative importance in driving variation in ecosystem function at large scales in diverse forests is unknown. Using 90 inventory plots across intact, lowland, terra firme, Amazonian forests and a new phylogeny including 526 angiosperm genera, we investigated the association between taxonomic and evolutionary metrics of diversity and two key measures of ecosystem function: aboveground wood productivity and biomass storage. While taxonomic and phylogenetic diversity were not important predictors of variation in biomass, both emerged as independent predictors of wood productivity. Amazon forests that contain greater evolutionary diversity and a higher proportion of rare species have higher productivity. While climatic and edaphic variables are together the strongest predictors of productivity, our results show that the evolutionary diversity of tree species in diverse forest stands also influences productivity. As our models accounted for wood density and tree size, they also suggest that additional, unstudied, evolutionarily correlated traits have significant effects on ecosystem function in tropical forests. Overall, our pan-Amazonian analysis shows that greater phylogenetic diversity translates into higher levels of ecosystem function: tropical forest communities with more distantly related taxa have greater wood productivity. Inventory data from 90 lowland Amazonian forest plots and a phylogeny of 526 angiosperm genera were used to show that taxonomic and phylogenetic diversity are both predictive of wood productivity but not of biomass variation.

Location of plots Location of 90 1-ha permanent inventory plots shown on a forest cover map⁷² produced from global land cover data. Plots are all located in lowland moist forests on well-drained soils across the Amazon Basin (see Methods for details).

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Bivariate relationships between aboveground wood productivity (AGWP) and the diversity variables included in the best-performing model a,b, Relationship between AGWP and Simpson index (a) and neighbour lineage diversity (b), using data from 90 1-ha plots across Amazonia. Shaded areas represent 95% confidence intervals. Solid lines indicate significant bivariate correlations between productivity and diversity metrics. Relationships for the other taxonomic and phylogenetic diversity metrics are included in the Supplementary Information.

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Standardized effect sizes for the best-fit model for both wood productivity and aboveground biomass Both models include diversity metrics, structural attributes, climate and soil variables selected based on the lowest AIC values and largest proportion of variance explained. The best model for AGWP includes neighbour lineage diversity and the Simpson index as biodiversity metrics, as well as mean annual temperature, climatic water deficit, total phosphorus, magnesium and potassium. Greater productivity is found in plots with lower mean annual temperature and higher water availability, and on soils with greater amounts of soil phosphorus and magnesium and lower amounts of potassium. The best model for aboveground biomass (AGB) included wood density, number of stems, magnesium and mean annual temperature. The relationship between AGB and wood density is nonlinear. In all AGB analyses, wood density was specified with linear and quadratic terms, but for clarity, in the graph, the effect size is shown only for the quadratic term. For each variable in the model, dots represent the standardized effect size and lines represent 1 s.d. In some cases, error lines are unobserved due to very small standard deviations. See Supplementary Figs. 4 and 7 for detailed bivariate correlations, and Supplementary Appendix 4 for all of the coefficients of the models.

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Articles

https://doi.org/10.1038/s41559-019-1007-y

Higher levels of taxonomic and phylogenetic diversity play

important and independent roles in determining ecosys-

tem function1–3. In experimental studies of temperate grass-

lands, higher levels of taxonomic and evolutionary diversity were

associated with greater biomass and productivity and variability in

the amount of evolutionary history shared within a group of spe-

cies was often a better predictor of productivity than the number of

species2–4, consistent with the hypothesis that evolutionary dissimi-

larity is related to niche complementarity1–5. However, although the

results of a range of biodiversity experiments2–7 suggest that com-

munities with distantly related lineages have greater carbon stocks

and productivity, the effect of phylogenetic diversity on measures

of ecosystem function remains controversial. Positive relationships

are common, but not a rule, and negligible effects of evolutionary

diversity on productivity and biomass have been reported in some

cases8,9. Therefore, it is still unclear whether these relationships can

be generalized, and the extent to which evolutionarily diverse com-

munities maximize function is unknown, particularly at large scales

relevant to conservation planning.

The total amount of phylogenetic diversity represented by spe-

cies within a community may be valuable for understanding how

diversity affects ecosystem function, because these properties

Evolutionary diversity is associated with wood

productivity in Amazonian forests

Fernanda Coelho de Souza 1*, Kyle G. Dexter2,3, Oliver L. Phillips 1, R. Toby Pennington3,4,

Danilo Neves 5, Martin J. P. Sullivan 1, Esteban Alvarez-Davila6, Átila Alves7, Ieda Amaral7,

Ana Andrade8, Luis E. O. C. Aragao4,9, Alejandro Araujo-Murakami10, Eric J. M. M. Arets 11,

Luzmilla Arroyo10, Gerardo A. Aymard C.12, Olaf Bánki13, Christopher Baraloto 14, Jorcely G. Barroso15,

Rene G. A. Boot16, Roel J. W. Brienen 1, Foster Brown17, José Luís C. Camargo8, Wendeson Castro 18,

Jerome Chave19, Alvaro Cogollo20, James A. Comiskey 21,22, Fernando Cornejo-Valverde23,

Antonio Lola da Costa24, Plínio B. de Camargo25, Anthony Di Fiore 26, Ted R. Feldpausch 4,

David R. Galbraith1, Emanuel Gloor1, Rosa C. Goodman27, Martin Gilpin1, Rafael Herrera28,29,

Niro Higuchi30, Eurídice N. Honorio Coronado31, Eliana Jimenez-Rojas32, Timothy J. Killeen33,

Susan Laurance34, William F. Laurance34, Gabriela Lopez-Gonzalez1, Thomas E. Lovejoy35,

Yadvinder Malhi 36, Beatriz S. Marimon37, Ben Hur Marimon-Junior37, Casimiro Mendoza38,

Abel Monteagudo-Mendoza39, David A. Neill40, Percy Núñez Vargas41, Maria C. Peñuela Mora42,

Georgia C. Pickavance1, John J. Pipoly III43, Nigel C. A. Pitman44, Lourens Poorter 45, Adriana Prieto46,

Freddy Ramirez47, Anand Roopsind48, Agustin Rudas46, Rafael P. Salomão 49,50, Natalino Silva50,

Marcos Silveira51, James Singh52, Juliana Stropp53, Hans ter Steege 13,54, John Terborgh55,56,

Raquel Thomas-Caesar57, Ricardo K. Umetsu 37, Rodolfo V. Vasquez39, Ima Célia-Vieira49,

Simone A. Vieira 58, Vincent A. Vos 59,60, Roderick J. Zagt16 and Timothy R. Baker1

Higher levels of taxonomic and evolutionary diversity are expected to maximize ecosystem function, yet their relative impor-

tance in driving variation in ecosystem function at large scales in diverse forests is unknown. Using 90 inventory plots across

intact, lowland, terra firme, Amazonian forests and a new phylogeny including 526 angiosperm genera, we investigated the

association between taxonomic and evolutionary metrics of diversity and two key measures of ecosystem function: above-

ground wood productivity and biomass storage. While taxonomic and phylogenetic diversity were not important predictors

of variation in biomass, both emerged as independent predictors of wood productivity. Amazon forests that contain greater

evolutionary diversity and a higher proportion of rare species have higher productivity. While climatic and edaphic variables

are together the strongest predictors of productivity, our results show that the evolutionary diversity of tree species in diverse

forest stands also influences productivity. As our models accounted for wood density and tree size, they also suggest that addi-

tional, unstudied, evolutionarily correlated traits have significant effects on ecosystem function in tropical forests. Overall, our

pan-Amazonian analysis shows that greater phylogenetic diversity translates into higher levels of ecosystem function: tropical

forest communities with more distantly related taxa have greater wood productivity.

A full list of affiliations appears at the end of the paper.

NATURE ECOLOGY & EVOLUTION | VOL 3 | DECEMBER 2019 | 1754–1761 | www.nature.com/natecolevol

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Article

Jan 2017

Plant functional traits are viewed as key to predicting important ecosystem and community properties across resource gradients within and among biogeographic regions. Vegetation dynamics and ecosystem processes, such as aboveground net primary productivity (ANPP), are increasingly being modeled as a function of the quantitative traits of species, which are used as proxies for photosynthetic rates and nutrient and water-use efficiency. These approaches rely on an assumption that a certain trait value consistently confers a specific function or response under given environmental conditions. Here, we provide a critical test of this idea and evaluate whether the functional traits that drive the well-known relationship between precipitation and ANPP differ between systemswith distinct biogeographic histories and species assemblages. Specifically, we compared grasslands spanning a broad precipitation gradient (∼200–1,000 mm/y) in North America and South Africa that differ in the relative representation and abundance of grass phylogenetic lineages. We found no significant difference between the regions in the positive relationship between annual precipitation and ANPP, yet the trait values underlying this relationship differed dramatically. Our results challenge the trait-based approach to predicting ecosystem function by demonstrating that different combinations of functional traits can act to maximize ANPP in a given environmental setting. Further, we show the importance of incorporating biogeographic and phylogenetic history in predicting community and ecosystem properties using traits.