The effect of parameter variability in the allometric projection of leaf growth rates for eelgrass (Zostera marina L.) II: the importance of data quality control procedures in bias reduction

Abstract

BackgroundEelgrass grants important ecological benefits including a nursery for waterfowl and fish species, shoreline stabilization, nutrient recycling and carbon sequestration. Upon the exacerbation of deleterious anthropogenic influences, re-establishment of eelgrass beds has mainly depended on transplantation. Productivity estimations provide valuable information for the appraisal of the restoration of ecological functions of natural populations. Assessments over early stages of transplants should preferably be nondestructive. Allometric scaling of eelgrass leaf biomass in terms of matching length provides a proxy that reduces leaf biomass and productivity estimations to simple measurements of leaf length and its elongation over a period. We examine how parameter variability impacts the accuracy of the considered proxy and the extent on what data quality and sample size influence the uncertainties of the involved allometric parameters.MethodsWe adapted a Median Absolute Deviation data quality control procedure to remove inconsistencies in the crude data. For evaluating the effect of parametric uncertainty we performed both a formal exploration and an analysis of the sensitivity of the allometric projection method to parameter changes. We used parameter estimates obtained by means of nonlinear regression from crude as well as processed data.ResultsWe obtained reference leaf growth rates by allometric projection using parameter estimates produced by the crude data, and then considered changes in fitted parameters bounded by the modulus of the vector of the linked standard errors, we found absolute deviations up to 10 % of reference values. After data quality control, the equivalent maximum deviation was under 7 % of corresponding reference rates. Therefore, the addressed allometric method is robust. Even the smaller sized samples in the quality controlled dataset produced better accuracy levels than the whole set of crude data.ConclusionsWe propose quality control of data as a highly recommended step in the overall procedure that leads to reliable allometric surrogates of eelgrass leaf growth rates. The proliferation of inconsistent replicates in the crude data points towards the importance of discarding incomplete leaves. We also recommend avoiding errors in estimating the biomass of small leaves for which precision of the used analytical scale might be an issue.

A Generalized Model of Complex Allometry I: Formal Setup, Identification Procedures and Applications to Non-Destructive Estimation of Plant Biomass Units

“…However, this approach is not also problem-free. Factors like analysis method sample size and data quality can bear significant influences on precision [25][26][27][28][29][30][31][32][33]. Understanding the way procedural factors drive precision of allometric projection of plant biomass units is crucial for assuring reliability.…”

On the suitability of an allometric proxy for nondestructive estimation of average leaf dry weight in eelgrass shoots I: sensitivity analysis and examination of the influences of data quality, analysis method, and sample size on precision

“…Allometric methods make it possible simplified-indirect estimations of eelgrass productivity and standing stock. Echavarría-Heras et al [ 30 ] considered an allometric representation for eelgrass leaf biomass and related length. Agreeing with Solana et al [ 31 ], the involved parameters are invariant within a given geographical region.…”

“…Leaf growth rates estimation relies on successive measurements of leaf biomass. Then the allometric model in [ 30 ] entails nondestructive assessments of eelgrass productivity. But, invariance does not impede local factors to imply variability of parameter estimates.…”

“…[ 32 , 33 ]). And, since scaling relationships are particularly sensitive to parametric uncertainties, Echavarría-Heras et al [ 30 ] concluded that the actual precision of derived allometric surrogates requires clarification.…”

“…Leaf area is more informative of eelgrass leaf biomass than corresponding length. Thus, the present scaling endures a boost in precision of parameter estimates by the model in [ 30 ]. This could increase the accuracy of derived surrogates for leaf biomass in shoots.…”