Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe

TRY plant trait database – enhanced coverage and open access

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

Data from: Definition and estimation of vital rates from repeated censuses: choices, comparisons and bias corrections focusing on trees

1.Mortality and recruitment rates are fundamental measures of population dynamics. Ecologists and others have defined and estimated these vital rates in various ways. We review these alternatives focusing on tree population census data in fixed area plots, though many aspects have wider application when similar data characteristics and assumptions apply: our goal is to guide choices and facilitate comparisons. 2.We divide our estimates into ‘instantaneous’ and ‘annual’ rates, corresponding to continuous- or discrete-time dynamics respectively. In each case, vital rate estimates can be further divided into those based on population density (‘per-capita’ rates) and those based on census area (‘per-area’ rates). We also examine how all such rate estimates relate to each other and can thus be interconverted and compared. 3.In a heterogeneous population (e.g., trees in forest stand) comprising subpopulations (e.g., species, locations, exposure classes), estimates of vital rates that assume homogeneity (equal likelihood of mortality and equal likelihood of recruitment for all individuals) are biased towards lower vital rates in stable mixed populations (due to survivorship bias) and towards lower absolute values of population change rate (due to changing-frequency bias). 4.We describe and illustrate an individual-based Bayesian procedure for estimating vital rates that reduces biases by accounting for demographic heterogeneity and sampling errors among and within subpopulations

Yakushima30

Sample data file for S1: stem census data in Yakushima Island (four sites) in 1983 and 2013

Supporting Information S1

R tutorial for the Bayesian estimation of vital rate

Estimating net biomass production and loss from repeated measurements of trees in forests and woodlands: Formulae, biases and recommendations

There is widespread interest in ensuring that assessment and knowledge of changes in forest biomass, and associated carbon gains or losses, are accurate and unbiased. Repeated measurements of individually-marked trees in permanent plots permit the estimation of rates of biomass production by tree growth and recruitment and of loss from mortality. But there are challenges, for example, simple estimates of production rate (i.e., the sum of biomass gain by growth of surviving trees and new recruits divided by census duration) decline as the census interval increases due to unrecorded growth. Even if we allow for these unobserved changes, additional biases may arise due to the non-independence of growth and mortality and to the heterogeneity and compositional changes within the forest. Here we examine these issues and demonstrate how problems can be minimized. We provide and compare alternative approaches to estimate net biomass production and loss from tree growth and mortality. Under the assumption that specific rates of biomass production and loss, i.e., turnover, are constant over time, we derive estimates of absolute biomass turnover rates that are independent of census duration. We show census-interval dependence of simple turnover rates grows with increasing specific turnover rates. While the time-dependent bias in simple estimates has previously been suggested to increase in proportion to the square of production, we show this relationship is approximately linear. Correlations between stem growth and mortality do not influence our estimates. We account for biomass gain by recruited stems without discounting their initial biomass in production estimates. We can reduce additional biases by accounting for differences in turnover among subpopulations (such as species, sites) and changes in their abundances. We provide worked examples from four forests covering a range of conditions (in Indonesia and Japan) and show the effects of accounting for these biases. For example, over five years in an Indonesian rain forest, simple estimates and instantaneous estimates neglecting species heterogeneity underestimated production by 4.9% and 1.6%, respectively when compared to comprehensive (instantaneous species-structured) estimates

Demographic Properties Shape Tree Size Distribution in a Malaysian Rain Forest

Different mechanisms have been proposed to explain how vertical and horizontal heterogeneity in light conditions enhances tree species coexistence in forest ecosystems. The foliage partitioning theory proposes that differentiation in vertical foliage distribution, caused by an interspecific variation in mortality-to-growth ratio, promotes stable coexistence. In contrast, successional niche theory posits that horizontal light heterogeneity, caused by gap dynamics, enhances species coexistence through an interspecific trade-off between growth rate and survival. To distinguish between these theories of species coexistence, we analyzed tree inventory data for 370 species from the 50-ha plot in Pasoh Forest Reserve, Malaysia. We used community-wide Bayesian models to quantify size-dependent growth rate and mortality of every species. We compared the observed size distributions and the projected distributions from size-dependent demographic rates. We found that the observed size distributions were not simply correlated with the rate of population increase but were related to demographic properties such as size growth rate and mortality. Species with low relative abundance of juveniles in size distribution showed high growth rate and low mortality at small tree sizes and low per-capita recruitment rate. Overall, our findings were in accordance with those predicted by foliage partitioning theory

Definition and estimation of vital rates from repeated censuses: Choices, comparisons and bias corrections focusing on trees

Mortality and recruitment rates are fundamental measures of population dynamics. Ecologists and others have defined and estimated these vital rates in various ways. We review these alternatives focusing on tree population census data in fixed area plots, though many aspects have wider application when similar data characteristics and assumptions apply: our goal is to guide choices and facilitate comparisons. We divide our estimates into “instantaneous” and “annual” rates, corresponding to continuous or discrete time dynamics, respectively. In each case, vital rate estimates can be further divided into those based on population density (“per-capita” rates) and those based on census area (“per-area” rates). We also examine how all such rate estimates relate to each other and can thus be interconverted and compared. In a heterogeneous population (e.g. trees in a forest stand) comprising subpopulations (e.g. species, locations, exposure classes), estimates of vital rates that assume homogeneity (equal likelihood of mortality and equal likelihood of recruitment for all individuals) are biased towards lower vital rates in stable mixed populations (due to survivorship bias) and towards lower absolute values of population change rate (due to changing-frequency bias). We describe and illustrate an individual-based Bayesian procedure for estimating vital rates that reduces biases by accounting for demographic heterogeneity and sampling errors among and within subpopulations