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A model for the biomass-density dynamics of seagrasses developed and calibrated on global data.

ABSTRACT: BACKGROUND:Seagrasses are foundation species in estuarine and lagoon systems, providing a wide array of services for the ecosystem and the human population. Understanding the dynamics of their stands is essential in order to better assess natural and anthropogenic impacts. It is usually considered that healthy seagrasses aim to maximize their stand biomass (g DW m-2) which may be constrained by resource availability i.e., the local environment sets a carrying capacity. Recently, this paradigm has been tested and reassessed, and it is believed that seagrasses actually maximize their efficiency of space occupation-i.e., aim to reach an interspecific boundary line (IBL)-as quick as possible. This requires that they simultaneously grow in biomass and iterate new shoots to increase density. However, this strategy depresses their biomass potential. RESULTS:to comply with this new paradigm, we developed a seagrass growth model that updates the carrying capacities for biomass and shoot density from the seagrass IBL at each time step. The use of a joint biomass and density growth rates enabled parameter estimation with twice the sample sizes and made the model less sensitive to episodic error in either of the variables. The use of instantaneous growth rates enabled the model to be calibrated with data sampled at widely different time intervals. We used data from 24 studies of six seagrass species scattered worldwide. The forecasted allometric biomass-density growth trajectories fit these observations well. Maximum growth and decay rates were found consistently for each species. The growth rates varied seasonally, matching previous observations. CONCLUSIONS:State-of-art models predicting both biomass and shoot density in seagrass have not previously incorporated our observation across many seagrass species that dynamics depend on current state relative to IBL. Our model better simulates the biomass-density dynamics of seagrass stands while shedding light on its intricacies. However, it is only valid for established patches where dynamics involve space-filling, not for colonization of new areas.

SUBMITTER: Vieira VMNCS

PROVIDER: S-EPMC6346591 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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A model for the biomass-density dynamics of seagrasses developed and calibrated on global data.

Vieira Vasco M N C S VMNCS Lopes Inês E IE Creed Joel C JC

BMC ecology 20190125 1

<h4>Background</h4>Seagrasses are foundation species in estuarine and lagoon systems, providing a wide array of services for the ecosystem and the human population. Understanding the dynamics of their stands is essential in order to better assess natural and anthropogenic impacts. It is usually considered that healthy seagrasses aim to maximize their stand biomass (g DW m<sup>-2</sup>) which may be constrained by resource availability i.e., the local environment sets a carrying capacity. Recentl ...[more]

PMID: 30683077

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Project description:Rationale and objectivesMammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.Materials and methodsA matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.ResultsIn adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.ConclusionThe calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.

Dataset Information

A model for the biomass-density dynamics of seagrasses developed and calibrated on global data.

Publications

A model for the biomass-density dynamics of seagrasses developed and calibrated on global data.

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