Estimate growth, size, maturity, mortality, stock-recruit, and population-dynamics parameters for all fish species globally

A graphical user interface (GUI) is available online

# Install and load package
devtools::install_github("james-thorson/FishLife")
library( FishLife )

Please see the FishLife vignette for details on how to extract predictions frmo the package, update predictions using new data, or replicate the analysis using a new data set.

vignette("tutorial","FishLife")

I also show a few simple examples of life-history predictions using FishLife, as archived in the package.

# Get basic plot for Lutjanus campechanus (in database, so prediction is informed by species-specific data)
Plot_taxa( Search_species(Genus="Lutjanus",Species="campechanus")$match_taxonomy )

# Get basic plot for Sebastes cortezi (not in database, so uses predictive distribution for genus Sebastes)
Plot_taxa( Search_species(Genus="Sebastes",Species="cortezi")$match_taxonomy )

# Get basic plot and extract values for Family Scombridae 
( Predictions = Plot_taxa(Search_species(Family="Scombridae")$match_taxonomy) )

You can also see the full set of parameters calculated for each taxon, either for internal use of anticipated to be useful for users:

head(FishLife::FishBase_and_RAM$beta_gv)

These can similarly be extracted and plotted:

params = matrix( c("K","M", "G","ln_MASPS"), ncol=2, byrow=TRUE)
Plot_taxa( Search_species(Genus="Lutjanus",Species="campechanus")$match_taxonomy, params=params )

while other values (e.g., slope at the origin for the Beverton-Holt stock recruit curve) can then be calculated from the set of available parameters.

By default FishLife uses the most-recent version published. This currently includes both growth, size, maturity, and mortality parameters from FishBase, as well as stock-recruit parameters estimated using the RAM Legacy stock-recruit database. To use earlier versions, use the Database argument in each function:

# Get basic plot for Lutjanus campechanus (in database, so prediction is informed by species-specific data)
Plot_taxa( Search_species(Genus="Lutjanus",Species="campechanus")$match_taxonomy, Database="FishBase" )

or expliclty use the updated database using:

Plot_taxa( Search_species(Genus="Lutjanus",Species="campechanus")$match_taxonomy, Database="FishBase_and_RAM" )`

• Thorson, J. T. In press. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data-integrated life-history model. Fish and Fisheries.
• Thorson, J. T., S. B. Munch, J. M. Cope, and J. Gao. 2017. Predicting life history parameters for all fishes worldwide. Ecological Applications. 27(8): 2262–2276. https://onlinelibrary.wiley.com/doi/10.1002/eap.1606/full

• Thorson, J. T., J. M. Cope, and W. S. Patrick. 2014. Assessing the quality of life history information in publicly available databases. Ecological Applications 24:217–226. https://onlinelibrary.wiley.com/doi/10.1890/12-1855.1/abstract

Presentation of research program available online

• Sea mullet, east coast Australia, Fisheries Queensland 2022 (link here)
• Spanish mackerel, eastern Australia, Fisheries Queensland 2021 (link here)
• Uku, Hawaii, PIFSC, 2020 (link here)
• Pollock, Eastern Bering Sea, AFSC, 2018 (link here)
• Black Marlin, Indian Ocean, IOTC, 2018 (link here)
• Striped Marlin, Indian Ocean, IOTC, 2018 (link here)
• Smoothound shark, South Africa, DAFF, 2018 (link here)
• Soupfin shark, South Africa, DAFF, 2018 (link here)
• Yang, W.-H., Martin, T.S., Moffitt, D., 2022. Stock assessment of Queensland east coast dusky flathead (Platycephalus fuscus), Australia, with data to December 2020.

1. Auber, A., Waldock, C., Maire, A., Goberville, E., Albouy, C., Algar, A.C., McLean, M., Brind’Amour, A., Green, A.L., Tupper, M., Vigliola, L., Kaschner, K., Kesner-Reyes, K., Beger, M., Tjiputra, J., Toussaint, A., Violle, C., Mouquet, N., Thuiller, W., Mouillot, D., 2022. A functional vulnerability framework for biodiversity conservation. Nat. Commun. 13, 4774. https://doi.org/10.1038/s41467-022-32331-y

2. Fitz, K.S., Montes Jr., H.R., Thompson, D.M., Pinsky, M.L., n.d. Isolation-by-distance and isolation-by-oceanography in Maroon Anemonefish (Amphiprion biaculeatus). Evol. Appl. n/a. https://doi.org/10.1111/eva.13448

3. Fujiwara, M., Simpson, A., Torres-Ceron, M., Martinez-Andrade, F., 2022. Life-history traits and temporal patterns in the incidence of coastal fishes experiencing tropicalization. Ecosphere 13, e4188. https://doi.org/10.1002/ecs2.4188

4. Hay, A., Riggins, C.L., Heard, T., Garoutte, C., Rodriguez, Y., Fillipone, F., Smith, K.K., Menchaca, N., Williamson, J., Perkin, J.S., 2022. Movement and mortality of invasive suckermouth armored catfish during a spearfishing control experiment. Biol. Invasions. https://doi.org/10.1007/s10530-022-02834-2

5. Hirota, D.S., Haimovici, M., Sant’Ana, R., Mourato, B.L., Santos, E.K., Cardoso, L.G., 2022. Life history, population dynamics and stock assessment of the bycatch species Brazilian flathead (Percophis brasiliensis) in southern Brazil. Reg. Stud. Mar. Sci. 102597. https://doi.org/10.1016/j.rsma.2022.102597

6. Mora, P., Figueroa-Muñoz, G., Cubillos, L., Strange-Olate, P., 2022. A data-limited approach to determine the status of the artisanal fishery of sea silverside in southern Chile. Mar. Fish. Sci. MAFIS 35, 275–298.

7. Omori, K.L., Tribuzio, C.G., Babcock, E.A., Hoenig, J.M., 2021. Methods for Identifying Species Complexes Using a Novel Suite of Multivariate Approaches and Multiple Data Sources: A Case Study With Gulf of Alaska Rockfish. Front. Mar. Sci. 1084.

8. Pawluk, M., Fujiwara, M., Martinez-Andrade, F., 2022. Climate change linked to functional homogenization of a subtropical estuarine system. Ecol. Evol. 12, e8783. https://doi.org/10.1002/ece3.8783

9. Pons, M., Cope, J.M., Kell, L.T., 2020. Comparing performance of catch-based and length-based stock assessment methods in data-limited fisheries. Can. J. Fish. Aquat. Sci. 77, 1026–1037. https://doi.org/10.1139/cjfas-2019-0276

10. Rudd, M.B., Thorson, J.T., Sagarese, S.R., 2019. Ensemble models for data-poor assessment: accounting for uncertainty in life-history information. ICES J. Mar. Sci. 76, 870–883. https://doi.org/10.1093/icesjms/fsz012

11. Safaraliev, I.A., Popov, N.N., 2022. Qualitative Assessment of the Stock Status of Freshwater Bream Abramis brama (Cyprinidae) from the Ural Stock Based on the LB-SPR Method. J. Ichthyol. 62, 476–486. https://doi.org/10.1134/S0032945222030134

12. Thorson, J.T., 2020. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data-integrated life-history model. Fish Fish. 21, 237–251. https://doi.org/10.1111/faf.12427

13. Thorson, J.T., Munch, S.B., Cope, J.M., Gao, J., 2017. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276. https://doi.org/10.1002/eap.1606