This meta data describes the raw output of the SES DEB-IBM built in NetLogo (version 6.0.1, March 2017; available from http://modelingcommons.org/browse/one_model/5348). The raw output consists of .csv files from several model runs. The detailed explanation of the model workings and background are published in Goedegebuure et al. (2018, PLoS ONE; Modelling southern elephant seals Mirounga leonina using an individual-based model coupled with a dynamic energy budget; DOI: 10.1371/journal.pone.0194950). In short: we developed an individual-based model which is coupled with a dynamic energy budget (a DEB-IBM) for southern elephant seals to demonstrate a method for detailed representation of marine mammals. We aimed to develop a model which could i) simulate energy use and life histories, as well as breeding traits of southern elephant seals in an emergent manner, ii) project a stable population over time, and iii) have realistic population dynamics and structure based on emergent life history features (such as age at first breeding, lifespan, fecundity and (yearling) survival). We evaluated the model's ability to represent a stable population over long time periods (> 10 generations), including the sensitivity of the emergent properties to variations in key parameters. The model was developed using life history data of female southern elephant seals from Macquarie Island and follows individuals from birth to death. The information collected in the raw output are the same for the baseline model (stable, and with standard parameters), and the modified models to test for 1) low, and 2) high food availability, 3) low, and 4) high weaning thresholds (energetic level at which pups transition to juveniles), 5) low, and 6) high puberty thresholds (energetic level at which juveniles transition to physically mature adults). As well as recording the parameter values as set in the model, each .csv file records: 1) run number (usually 1-10) 2) step (time step, days) 3) min age at first breeding (years) 4) min age of adult 5) mean age of adult 6) mean age of juvenile 7) max age of individuals 8) max number of pups per female 9) fecundity 10) max size of individuals 11) mean size of adults 12) mean size of juveniles 13) total count of modelled population 14) total count of embryos 15) total count of pups 16) total count of yearlings 17) total count of juveniles (includes yearlings) 18) total count of adults 19) mean food availability of independent individuals (those not reliant on their mother) that are not fasting/moulting 20) carrying capacity (or expected equilibrium) 21) seed NB. NetLogo calls individuals within the model turtles - thus output will mention turtles. Stages are as follows 0 = foetus, 1 = pup, 2 = juvenile, 3 = adult. Status are as follows, 0 = dependent on mother, 1 = fasting, 2 = foraging.

Sediment derived from the decomposition of Halimeda algae is a major contributor to the tropical back-reef carbonate depositions. Halimeda bioherms occur extensively on the northern Great Barrier Reef (GBR), Australia. This dataset represents the most complete, high-resolution spatial mapping of the extent of northern GBR Halimeda bioherms, based on airborne lidar and multibeam echosounder bathymetry data. Three distinct morphological sub-types are described: reticulate, annulate, and undulate. The northern GBR bioherms cover an area of 6095 km2, three times larger than previous estimates.

To test if the carapace length of lobsters changes during cooking, 21 legal-sized southern rock lobsters were collected in pots from Alum Cliffs, south-eastern Tasmania, Australia in October 1999 (42.95±S 147.35±E). The sample consisted of 7 female and 14 male lobsters ranging in carapace length from 106 mm to 153 mm (mean 120 mm). Each animal was abdominally tagged using individually marked t-bar tags (Hallprint T-bar anchor tag, TBA1; Hallprint Pty Ltd, 27 Jacobsen Crescent, Holden Hill, SA 5088, Australia). The carapace length of all lobsters were measured five times to the nearest 0.1 mm in a random manner, before and after processing. This repeated measurement of all specimens in random order was intended to evaluate measurement error. Processing was typical of that used commercially and involved killing the lobsters in fresh water before cooking in pre-boiling salted water for 12 minutes.

Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (𝘌𝘶𝘱𝘩𝘢𝘶𝘴𝘪𝘢 𝘴𝘶𝘱𝘦𝘳𝘣𝘢) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions has led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. Here we present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management. ____ This dataset represents KRILLPODYM modelled estimates of Antarctic krill circumpolar biomass distribution for the final year of a 12-year spin up. Biomass distributions are given for each of the five key life stages outlined above. The accompanying background, model framework and initialisation description can be found in the following reference paper: 𝗚𝗿𝗲𝗲𝗻 𝗗𝗕, 𝗧𝗶𝘁𝗮𝘂𝗱 𝗢, 𝗕𝗲𝘀𝘁𝗹𝗲𝘆 𝗦, 𝗖𝗼𝗿𝗻𝗲𝘆 𝗦𝗣, 𝗛𝗶𝗻𝗱𝗲𝗹𝗹 𝗠𝗔, 𝗧𝗿𝗲𝗯𝗶𝗹𝗰𝗼 𝗥, 𝗖𝗼𝗻𝗰𝗵𝗼𝗻 𝗔, & 𝗟𝗲𝗵𝗼𝗱𝗲𝘆 𝗣. (2023) KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance. 𝘍𝘳𝘰𝘯𝘵𝘪𝘦𝘳𝘴 𝘪𝘯 𝘔𝘢𝘳𝘪𝘯𝘦 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, 10 Article 1218003. https://doi.org/10.3389/fmars.2023.1218003