ALBERT

Description: These data comprise a grain character dataset (grain size, sorting and grain shape) from the topset, foreset, and bottomset deposits of four successive Miocene intrashelf clinothem sequences (m5.7, m5.4, m5.45 and m5.3). These clinothems have been mapped and described by various authors (e.g. Monteverde et al., 2008; Mountain et al., 2010; Miller et al., 2013), and were continuously cored and logged during IODP (International Ocean Discovery Program) Expedition 313 (Offshore New Jersey, USA). In total, 878 sediment samples were collected from the working half of three cores recovered during IODP Expedition 313, offshore New Jersey. The three cores, kept in cold storage at the University of Bremen, are from Sites M27, M28 and M29. The stratigraphic horizons targeted during this investigation were exclusively Miocene in age, corresponding to depths of 225 - 365 mcd (metres composite depth), 312 - 600 mcd, and 600 - 730 mcd in cores M27, M28 and M29 respectively. Collectively, a total of 560 m of core has been sampled. With reference to the seismic clinothem model presented in Miller et al. (2013), these stratigraphic depths correspond to the interval between major seismic sequence boundaries m5.7 - m5.2. Where no prominent grain size change was recorded in either the cumulative lithology presented in Miller et al. (2013) or core descriptions (Mountain et al., 2010), the strategy for sample collection was to remove 15 x 15 x 15 mm sediment slices, subsampled at ~ 500 mm intervals down-core. The sampling strategy was amended to target stratigraphic depths where grain size change was most prominent. At these intervals, highlighted by the broad patterns of down-core lithological and grain size change (Mountain et al., 2010; Browning et al., 2013; Miller et al., 2013), sampling density was increased to ~ 300 mm intervals. During the sampling process there was some deviation from this sampling configuration in order to avoid 1) horizons of cementation, (2) biscuiting disturbance, 3) key stratigraphic surfaces and 4) heavily sampled intervals. Due to the pervasive presence of biogenic material (including calcareous skeletal remains, shell fragments, and organic matter) sample pre-treatment was undertaken prior to grain character measurements, in order to remove these components. Sample pre-treatment comprised the careful manual disaggregation of the semi-lithified samples using an agate mortar and pestle (e.g., Sahu, 1964; Wilson and Pittman, 1977; Nelson, 1983; Frey and Payne, 1996; Ando et al., 2014). Hydrochloric acid (10% weight to volume) and hydrogen peroxide (30% weight to volume) were added to all samples, to ensure the removal of any calcareous and non-calcareous organic components, respectively (e.g., Battarbee, 1986; Battarbee et al., 2001; Gray et al., 2009) Grain character is defined as the grain size, sorting and grain shape (sphericity and roundness) of a sample. Grain character analysis was completed using a CamsizerXT (Retsch Technology), which is an optically based dynamic image analyser. The CamsizerXT is capable of measuring the grain-size range 1 µm - 8 mm (clay - gravel), with an accuracy of ± 1%. Grain-size fractions 〈 1 µm are lost during the process of analysis. The statistical analysis of all CamsizerXT results was completed using GRADISTAT computer software (Blott and Pye, 2001). The GRADISTAT software enables the rapid analysis of grain size statistics from multiple sediment samples and produces numerical, geometrically and logarithmically calculated values of the mean, mode, and sorting (more information on Page Two of data spreadsheet). Grain shape data were analysed using Microsoft Excel software. The data are presented in spreadsheet format. Each sample is given a 'Site' this refers to Sites M27, M28 and M29; for the site locations the user is asked to refer to the seismic clinothem model presented in Mountain et al. (2010) and Miller et al. (2013). Each sample is also given a core number and core section (e.g., 80-1) and a sample depth (given in meters composite depth; e.g., 225.55 mcd); the user is asked to refer to Mountain et al. (2010) and Browning et al. (2013) to see the sampled core numbers, sections and depths alongside sedimentary logs, completed by the Onshore Scientific Party of IODP Expedition 313. Each sample is given: i) a textural group and a sediment name; ii) an arithmetic, geometric and logarithmic value of the grain-size mean, standard deviation, skewness and kurtosis; iii) an arithmetic, geometric and logarithmic value of the sorting and iv) the percentage of each grain-size fraction present within a sample. On Page Two of the spreadsheet (entitled GRADISTAT Information); Tables One and Two outline the mathematical parameters and grain-size scale used by the GRADISTAT software (Blott and Pye, 2001) to calculate the grain character data. For more information the user is asked to refer to Blott and Pye (2001). A synthesis of the grain character results and interpretations have been published in two papers; Cosgrove et al. (2018) and Cosgrove et al. (2019). These papers document patterns of sediment dispersal and variations in grain size, sorting and grain shape, at a basin-scale (i.e. across successive clinothems) and within individual clinothem sequences (i.e. at an intra-clinothem scale). These data can be used to condition and validate process-based numerical forward models and have widespread applications in prediction of reservoir quality in both frontier and mature hydrocarbon basins.

Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Ecology 103 (2015): 202–218, doi:10.1111/1365-2745.12334.

Description: Schedules of survival, growth and reproduction are key life-history traits. Data on how these traits vary among species and populations are fundamental to our understanding of the ecological conditions that have shaped plant evolution. Because these demographic schedules determine population growth or decline, such data help us understand how different biomes shape plant ecology, how plant populations and communities respond to global change and how to develop successful management tools for endangered or invasive species. Matrix population models summarize the life cycle components of survival, growth and reproduction, while explicitly acknowledging heterogeneity among classes of individuals in the population. Matrix models have comparable structures, and their emergent measures of population dynamics, such as population growth rate or mean life expectancy, have direct biological interpretations, facilitating comparisons among populations and species. Thousands of plant matrix population models have been parameterized from empirical data, but they are largely dispersed through peer-reviewed and grey literature, and thus remain inaccessible for synthetic analysis. Here, we introduce the compadre Plant Matrix Database version 3.0, an open-source online repository containing 468 studies from 598 species world-wide (672 species hits, when accounting for species studied in more than one source), with a total of 5621 matrices. compadre also contains relevant ancillary information (e.g. ecoregion, growth form, taxonomy, phylogeny) that facilitates interpretation of the numerous demographic metrics that can be derived from the matrices. Large collections of data allow broad questions to be addressed at the global scale, for example, in genetics (genbank), functional plant ecology (try, bien, d3) and grassland community ecology (nutnet). Here, we present compadre, a similarly data-rich and ecologically relevant resource for plant demography. Open access to this information, its frequent updates and its integration with other online resources will allow researchers to address timely and important ecological and evolutionary questions.

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Animal Ecology 85 (2016): 371–384, doi:10.1111/1365-2656.12482.

Description: The open-data scientific philosophy is being widely adopted and proving to promote considerable progress in ecology and evolution. Open-data global data bases now exist on animal migration, species distribution, conservation status, etc. However, a gap exists for data on population dynamics spanning the rich diversity of the animal kingdom world-wide. This information is fundamental to our understanding of the conditions that have shaped variation in animal life histories and their relationships with the environment, as well as the determinants of invasion and extinction. Matrix population models (MPMs) are among the most widely used demographic tools by animal ecologists. MPMs project population dynamics based on the reproduction, survival and development of individuals in a population over their life cycle. The outputs from MPMs have direct biological interpretations, facilitating comparisons among animal species as different as Caenorhabditis elegans, Loxodonta africana and Homo sapiens. Thousands of animal demographic records exist in the form of MPMs, but they are dispersed throughout the literature, rendering comparative analyses difficult. Here, we introduce the COMADRE Animal Matrix Database, an open-data online repository, which in its version 1.0.0 contains data on 345 species world-wide, from 402 studies with a total of 1625 population projection matrices. COMADRE also contains ancillary information (e.g. ecoregion, taxonomy, biogeography, etc.) that facilitates interpretation of the numerous demographic metrics that can be derived from its MPMs. We provide R code to some of these examples. Synthesis: We introduce the COMADRE Animal Matrix Database, a resource for animal demography. Its open-data nature, together with its ancillary information, will facilitate comparative analysis, as will the growing availability of databases focusing on other aspects of the rich animal diversity, and tools to query and combine them. Through future frequent updates of COMADRE, and its integration with other online resources, we encourage animal ecologists to tackle global ecological and evolutionary questions with unprecedented sample size.

Description: Australian Research Council Grant Number: DE140100505; Evolutionary Demography Laboratory at the Max Planck Institute for Demographic Research (MPIDR); Natural Environmental Research Council Grant Number: NE/N006798/1