Abstract:

The data here are model simulation results of a hybrid size-spectra and metabolic theory model of general ocean ecosystems. This dataset supports the publication: Woodsen, C.B., Schramski, J.R., & Joye, S.B. (2018). A unifying theory for top-heavy ecosystem structure in the ocean. Nature Communications, 9(23). doi: 10.1038/s41467-017-02450-y

Suggested Citation:

C. Brock Woodson, Samantha Joye. 2018. Dataset for: A Unifying Theory for Top-Heavy Ecosystem Structure in the Ocean. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7959G1K

Publications:

Woodson, C. B., Schramski, J. R., & Joye, S. B. (2018). A unifying theory for top-heavy ecosystem structure in the ocean. Nature Communications, 9(1). doi:10.1038/s41467-017-02450-y

Purpose:

These data were generated to examine the impacts of large generalist predators (sharks and other large predators) and megaconsumers (whales, mobulid rays) on marine ecosystem structure.

Data Parameters and Units:

tte_biomass_genpred_final_20171116.nc Number of components in the ecosystem model (comp) Number of iterations for biomass and structure estimates with trophic efficiency varied randomly for each iteration (niter) Trophic transfer efficiency (tte, ratio) Energy based trophic web (flow_matrix, proportion of energy) Biomass density by trophic category (mass_dens, kg C m^-2) Total biomass density (total_mass, kg C m^-2) Organism mass (org_mass, kg C ind^-1) Woodsonetal_NatComm_PPMR_data.xlsx: Species Number of stomachs (n) Number of prey items (N) % of diet fish (FISH, %) % of diet cephalopods (CEPH, %) % of diet mollusks (MOL, %) % of diet crustaceans (CR, %) % of diet other invertebrates (INV, %) % of diet zooplankton (ZOO, %) % of diet birds (BIR, %) % of diet reptiles (REP, %) % of diet mammals (MAM, %) % of diet Chondrichthyan fish (CHON, %) % of diet plants (PL, %) Length (cm) a = Coefficient for length:weight relationship W=aL^b b = coefficient for length:weight relationship W=aL^b Mass (kg) Predator:Prey mass ratio (PPMR, ratio) Median PPMR (PPMRmed) Mean PPMR (PPMRmean) Large generalist predators (LGPs) Gigantic secondary consumers (GSCs) Note: the matlab code does not run after line 300, it requires the following code to run: function[Lo,Up]=confint(x,statfun,alpha,B1,B2,varargin) % % [Lo,Up]=confint(x,statfun,alpha,B1,B2,PAR1,...) % % Confidence interval of the estimator of a parameter % based on the bootstrap percentile-t method % % Inputs: % x - input vector data % statfun - the estimator of the parameter given as a Matlab function % alpha - level of significance (default alpha=0.05) % B1 - number of bootstrap resamplings (default B1=199) % B2 - number of bootstrap resamplings for variance % estimation (nested bootstrap) (default B2=25) % PAR1,... - other parameters than x to be passed to statfun % % Outputs: % Lo - The lower bound % Up - The upper bound % % Example: % % [Lo,Up] = confint(randn(100,1),'mean'); % Created by A. M. Zoubir and D. R. Iskander % May 1998 % % References: % % Efron, B.and Tibshirani, R. An Introduction to the Bootstrap. % Chapman and Hall, 1993. % % Hall, P. Theoretical Comparison of Bootstrap Confidence % Intervals. The Annals of Statistics, Vol 16, % No. 3, pp. 927-953, 1988. % % Zoubir, A.M. Bootstrap: Theory and Applications. Proceedings % of the SPIE 1993 Conference on Advanced Signal % Processing Algorithms, Architectures and Imple- % mentations. pp. 216-235, San Diego, July 1993. % % Zoubir, A.M. and Boashash, B. The Bootstrap and Its Application % in Signal Processing. IEEE Signal Processing Magazine, % Vol. 15, No. 1, pp. 55-76, 1998. pstring=varargin; if (exist('B2')~=1), B2=25; end; if (exist('B1')~=1), B1=199; end; if (exist('alpha')~=1), alpha=0.05; end; x=x(:); vhat=feval(statfun,x,pstring{:}); [vhatstar,ind]=bootstrp(B1,statfun,x,pstring{:}); if length(pstring)~=0, if length(pstring{:})==length(x) newpstring=pstring{:}; bstats=bootstrp(B2,statfun,x(ind),newpstring(ind)); else bstats=bootstrp(B2,statfun,x(ind),pstring{:}); end; else bstats=bootstrp(B2,statfun,x(ind),pstring{:}); end; bstat=bootstrp(B2,statfun,x,pstring{:}); sigma1=std(bstat); q1=floor(B1*alpha*0.5); q2=B1-q1+1; sigma=std(bstats)'; tvec=(vhatstar-vhat)./sigma; [st,ind]=sort(tvec); lo=st(q1); up=st(q2); Lo=vhat-up*sigma1; Up=vhat-lo*sigma1

Methods:

netcdflibversion=4.4.1.1

Instruments:

hdf5libversion=1.8.18' Dimensions: niter = 20000 comp = 6 ones = 1 Variables: org_mass Size: 6x1 Dimensions: comp Datatype: double Attributes: long_name = 'organism mass' units = 'kg C ind^-1' tte Size: 6x20000 Dimensions: comp,niter Datatype: double Attributes: long_name = 'trophic transfer efficiency' units = 'ratio' total_mass Size: 20000x1 Dimensions: niter Datatype: double Attributes: long_name = 'total biomass density' units = 'kg C m^-2' mass_dens Size: 6x20000 Dimensions: comp,niter Datatype: double Attributes: long_name = 'biomass density by trophic category' units = 'kg C m^-2' flow_matrix Size: 6x6x20000 Dimensions: comp,comp,niter Datatype: double Attributes: long_name = 'energy based trophic web' units = 'proportion of energy'

Individual Files: