Biomedical subjects

Remko A Duursma

Publications and source records attributed to Remko A Duursma.

3 recordsLinked to original sources

PubMed2005

Estimating leaf-level parameters for ecosystem process models: a study in mixed conifer canopies on complex terrain.

Ecosystem process models are often used to predict carbon flux on a landscape or on a global scale. Such models must be aggregate and canopies are often treated as a uniform unit of foliage. Parameters that are known to vary within the canopy, e.g., nitrogen content and leaf mass per area, are often estimated by a mean value for the canopy. Estimating appropriate means is complicated, especially in mixed-species stands and in complex terrain. We analyzed sources of variation in specific parameters with the goal of testing various simplifying assumptions. The measurements came from mixed-species forests in the northern Rocky Mountains. We found that, for three important parameters (nitrogen concentration and content, and leaf mass per area), a sample taken near the vertical center of the crown provided a good estimate of the mean values for the crown. Altitude (700-1700 m), solar insolation (4200-5400 MJ m(-2) year(-1)) and leaf area index (1-11) had negligible effects on the parameters; only species differences were consistently detected. The correlation between mass-based photosynthetic rates and mass-based nitrogen concentrations was much weaker than the correlation between area-based photosynthetic rates and area-based nitrogen concentration. Comparison of photosynthesis-nitrogen relationships for a wide variety of conifer species and sites revealed a broad general trend that can be used in models. These results suggest important potential simplifications in model parameterization, most notably that canopy means can be estimated with ease, that complex terrain is a minor source of variation in these parameters and that use of one photosynthesis-nitrogen relationship for conifer species does not result in large errors. Species-to-species variation, however, was large and needs to be accounted for when parameterizing process models.

Altitude↗

PubMed2005

Leaf longevity of western red cedar (Thuja plicata) increases with depth in the canopy.

Leaf longevity determines the annual cost of replacing foliage biomass and influences water and nutrient budgets. Longevity is readily estimated in most evergreen species by annual bud scars along the shoot. However, some species with indeterminate growth do not show these annual markers, making estimation of longevity difficult. One of these species is the widespread and economically valuable western red cedar (Thuja plicata J. Donn ex D. Don), for which no dependable estimates of leaf longevity exist. In this study, we estimated leaf longevity for western red cedar by counting growth rings in shoots at the point of leaf abscission. Estimates were obtained on 26 dominant or codominant trees growing in natural stands in a montane forest in northern Idaho, USA. Leaf longevity averaged 8.9 (SE = 0.2) years, but it strongly increased with depth in the canopy (0.3 year m(-1); mean crown depth was 15 m), increasing from a mean of 6.8 years in the upper third of the canopy to 10.6 years in the lower third. The increase in longevity with depth in the crown is consistent with many reports showing that longevity increases in resource-limiting environments. Longevity did not vary significantly with altitude or solar insolation in these montane forests. Among stand-level variables, longevity was correlated only with leaf area index: it increased slightly in stands with high leaf area indices. This approach to longevity estimation may be useful for any species that produces annual rings but no obvious bud scars, including many Cupressaceae species.

Altitude↗

PubMed2005

A regression-based equivalence test for model validation: shifting the burden of proof.

Model validation is often realized as a test of how well model predictions match a set of independent observations. One would think that the burden of proof should rest with the model, to force it to show that it can make accurate predictions. Further, one would think that increasing the sample size ought to increase the model's ability to demonstrate its utility. Traditional statistical tools are inappropriate for this because they default to the case that the model and the data are no different, and their ability to detect differences increases with the sample size. These traditional tools are optimized to detect differences, rather than similarities. We present an alternative strategy for model validation that is based on regression and statistical tests of equivalence. Equivalence tests reverse the usual null hypothesis: they posit that the populations being compared are different and use the data to prove otherwise. In this sense, equivalence tests are lumping tests, whereas the traditional statistical tests are splitting tests. To date, model validation with equivalence tests has focused on comparisons of means. Our proposed test checks not only for similarity of means, but also for similarity between individual predictions and observations. The strategy is demonstrated using three case studies that differ in their modeling objectives, and for varied sample sizes. The proposed strategy provides a formal means of model validation that is superior to traditional statistical tests in each case.

Forestry↗