Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

This dataset consists of one table with annual counts from population plots of Black-legged Kittiwakes and Common Murres at two seabird nesting colonies on Gull and Chisik Islands in lower Cook Inlet, Alaska.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

This Matlab code was used to produce figures and results for the manuscript: "Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions?" The main code can be used to compute the omega statistic presented in this paper. Key functions/scripts are: robustDecayTry.m: computes a p-correction for GMRF priors to omega compLowerBndsDecay.m: computes the bound on omega^2, used for model rejection modSelCurve.m, modSel_via_m.m, maxpModSel.m and omegaModelSelect.m: use to test model rejection algorithms under various conditions robustDecay.m and testEllip.m: looks at how Fisher information from prior influences the omega statistic and looks at uncertainty ellipses All files with the word 'King': computes omega statistic for Kingman coalescent *Update: now includes xmls for the empirical-based simulations for the bison and HCV examples in the revised Fig 5 and 6 of the main text.,In Bayesian phylogenetics, the coalescent process provides an informative framework for inferring changes in the effective size of a population from a phylogeny (or tree) of sequences sampled from that population. Popular coalescent inference approaches such as the Bayesian Skyline Plot, Skyride and Skygrid all model these population size changes with a discontinuous, piecewise-constant function but then apply a smoothing prior to ensure that their posterior population size estimates transition gradually with time. These prior distributions implicitly encode extra population size information that is not available from the observed coalescent data i.e. the tree. Here we present a novel statistic, Ω, to quantify and disaggregate the relative contributions of the coalescent data and prior assumptions to the resulting posterior estimate precision. Our statistic also measures the additional mutual information introduced by such priors. Using Ω we show that, because it is surprisingly easy to over-parametrise piecewise-constant population models, common smoothing priors can lead to overconfident and potentially misleading inference, even under robust experimental designs. We propose Ω as a useful tool for detecting when posterior estimate precision is overly reliant on prior choices.

This dataset is to visualize the 4TU.ResearchData resources for the plot-a-thon.

The LTERN Tropical Rainforest Plot Network Rainforest Tree Demographic Data contains stem measurement data for 1 of 20, 0.5 ha (100 m x 50 m) permanent rainforest plots in Northern Queensland, Australia from 2011 to 2013. This is part of a much larger dataset that spans from 1971 to 2013 that is managed by CSIRO. This data publication refers specifically to observations made at Plot EP19, and this data is accessible as a composite data package at the following location: Metcalfe, D; Murphy, H; Bradford, M; Hogan, D; Ford, A (2014): Tropical Rainforest Plot Network: Rainforest Tree Demographic Data, Northern Queensland, Australia, 2011-2013. Long Term Ecological Research Network. http://www.ltern.org.au/knb/metacat/ltern2.90.42/html The CSIRO permanent rainforest plots are located within 60 km of the north Queensland coast between Mackay (21.5ºS, 149ºE) and the Iron Range on Cape York Peninsula (12.5ºS, 143ºE). The plots have a rainfall range of 1200 to 3500 mm, represent eleven vegetation types, six parent materials, and range from 15 m to 1200 m above sea level. Except for minor disturbances associated with selective logging on two plots, the plots were established in old growth forest and all plots have thereafter been protected. Plots were regularly censused and at each census the diameter at breast height (DBH) of all stems ≥10 cm DBH is recorded. Due to the wide geographical range of the plots, no species dominate, although the families Lauraceae, Rutaceae and Myrtaceae contribute a large number of species. The data collected from the 20 plots provides an insight into the floristical composition, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale (Bradford, M.G., Murphy, H.T., Ford, A.J., Hogan, D. and Metcalfe, D.J. (2014). Long term stem inventory data from tropical rainforest plots in Australia. Ecology 95:2362. http://www.rainforest-crc.jcu.edu.au/publications/permanent_plots1.pdf. This is part of a much larger dataset that spans from 2004 to 2014; a synopsis of related data packages which have been collected as part of the Tropical Rainforest Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/tropical-rainforest.

Abstract: This rainforest tree demographic data package comprises recruitment, growth and mortality census data for rainforest trees Davies Creek Plot in Dinden National Park, (25 km south west of Cairns), Queensland for 1963-2013. This plot consists of one 1.7 hectare plot in tropical rainforest, established in 1963. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years. The Davies Creek Plot was incorporated over an existing 0.4 ha plot established by the Queensland Department of Forestry in 1951 (Nicholson et al. 1988), so the central part of the Davies Creek Plot has records extending back more than a decade prior to 1963.

This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963.

A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network’s full program is provided at https://doi.org/10.25911/5c13444388e1b

Sampling method: The Dinden National Park Plot is a 1.7 hectare plot. The plot was selected by Prof. Joseph H. Connell in 1963 on the advice of his CSIRO collaborators Dr Len Webb and Mr Geoff Tracey, and was chosen for three reasons; it was accessible, it was unlogged, and a smaller 0.4 ha plot belonging to the Queensland Department of Forestry had already been established there in 1951.

This plot is one of two plots established by Connell in 1963 – the other is in subtropical rainforest near O’Reilly’s Guesthouse in Lamington National Park, 65 km south of Brisbane. The same sampling methods are employed at both plots, at intervals of 1-6 years.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003.

Between 2012 and 2018 this project was part of, and funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California, USA, as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during 7 annual surveys consisting of two censuses each over a 34-year period. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. For this model we incorporated 3 size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary) and 3 survival states (not-yet-entered, entered/alive, and dead/removed). We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states. The model was incorporated into the manuscript that included several other databases for publication in Wildlife Monographs in 2020 by Berry et al.

Additional file 4. Code to create the plots in this paper presented as a R markdown file.

Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

Additional file 2. Input files needed to recreate the plots in this paper: Tracer output files for three species.

Seedling demography data are provided in annual censuses of 600 seedling plots in an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuní National Park. This long-term study uses standardized methodology from the Smithsonian ForestGEO network of plots, and in particular coordination with similar studies in Luquillo, Puerto Rico, and Barro Colorado Island, Panama. We address hypotheses about the maintenance of forest diversity and long-term variation, and link our data to companion studies of flowering and fruiting phenology and sapling and adult dynamics in the Yasuní Forest Dynamics 50-ha Plot. The project is ongoing, and additional data will be added as they are processed.

Additional file 3. Input files needed to recreate the plots in this paper: raw sequence data for alignment.

This dataset consists of one table with annual counts from population plots of Black-legged Kittiwakes and Common Murres at two seabird nesting colonies on Gull and Chisik Islands in lower Cook Inlet, Alaska.

Abstract: his rainforest tree data package comprises stand structure data for rainforest trees at the O'Reilly's Connell Rainforest Plot, Lamington National Park (84 km south of Brisbane), Queensland for 2015. The O'Reilly's Plot consists of two 1.0 hectare plots spaced 600 m apart in sub-tropical rainforest, established in 1963. They have always been treated as a single unit for the purpose of analysis. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years since 1963. It essentially provides a snapshot of stand structure on the site. This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963.

A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network's full program is provided at https://doi.org/10.25911/5c13444388e1b.

Sampling method: The O'Reilly's Plot consists of two 1.0 hectare plots spaced 600 m apart, which have always been treated as a single unit for the purpose of analysis. This data package forms part of the collection of vegetation data undertaken at plots in Lamington National Park which were initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963. The same sampling methods are employed in a related data package focussing on tropical rainforest plots at Davies Creek, Dinden National Park (1.7 ha, 25 km south-west of Cairns). Sampling has been undertaken at intervals of 1-6 years.

Project abstract: This group conducts research in the rainforest investigating tree demographics.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003.

Between 2012 and 2018 this project was part of, and funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

Long-term population patterns of coquies likely result from a variety of influences. Key among these are moisture, the physical habitat as affected by succession and disturbances of various scales, and predator population. Here, I present data on each of these factors along with a nine-year record of population numbers of coquies in four long-term study plots in the Luquillo Experimental Forest (LEF) of northeastern Puerto Rico.

Each dataset consists of three classes of entities: * the refined distribution of the population to the plot (polygons) * the refined distribution of the population to the built (polygons) * the refined distribution of the population to the centroid of the structure (punctual) The treatment is carried out from several data sources: * the IRIS GE INSEE population — 2018 (Latest year, production is n-3 compared to the current year) * CEREMA land files — 2021 (the vintage of the Cerema land files of year n is based on the MAJIC of year n and the MAJIC data of this vintage returns the data from year n) * the BDTopo IGN — 2021 * the Parcellaire Express IGN — 2021 We we worked with the most recent vintage on the population (2018) which does not match the IGN and Cerema data vintage (2021). It should therefore be borne in mind that this data does not show the actual distribution in 2018 but a potential distribution on the plots and buildings of the year 2020 (FF n-1). The sub-communal population is distributed in proportion to the living area of the plots. It is then ventilated on the significant buildings of each plot. The entire processing is described in the methodology that accompanies the data.OPenIG, the co-producer of the data, participated in the specifications, the industrialisation of the processing chain and the dissemination of the classes of entities. ** Warning** The operations that may be made of this data will be the sole responsibility of the user. It does not in any way provide the actual, exact or legal distribution of the population at the level of the building or parcel. Thus, use on this scale makes no sense (e.g. distribution of the population on the buildings of the same plot) as well as possible calculations of evolution between two dates. Authors: Montpellier Méditerranée Métropole, City of Montpellier, OPenIG ** Covered territory:** The data is delivered on a department-wide basis. To have the data on your department in the Occitanie region, do not hesitate to request it: webmestre@openig.org

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

Abstract: This rainforest tree data package comprises stand structure data for rainforest trees at the Davies Creek Plot in Dinden National Park, (25 km south west of Cairns), Queensland for 2015. This plot consists of one 1.7 hectare plot in tropical rainforest, established in 1963. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years since 1963; this data package contains seedling recruitment census data of the plot. This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963. A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network’s full program is provided at http://hdl.handle.net/1885/151946https://doi.org/10.25911/5c13444388e1b

Sampling method: The Dinden National Park Plot is a 1.7 hectare plot.The plot was selected by Prof. Joseph H. Connell in 1963 on the advice of his CSIRO collaborators Dr Len Webb and Mr Geoff Tracey, and was chosen for three reasons; it was accessible, it was unlogged, and a smaller 0.4 ha plot belonging to the Queensland Department of Forestry had already been established there in 1951. This plot is one of two plots established by Connell in 1963 – the other is in subtropical rainforest near O’Reilly’s Guesthouse in Lamington National Park, 65 km south of Brisbane. The same sampling methods are employed at both plots, at intervals of 1-6 years. See Connell Rainforest Plot Network’s full program provided at https://doi.org/10.25911/5c13444388e1b for further details.

Study extent: None

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003. Between 2012 and 2018 this project was solely funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

A major goal in ecology is to understand mechanisms that increase invasion success of exotic species. A recent hypothesis implicates altered species interactions resulting from ungulate herbivore overabundance as a key cause of exotic plant domination. To test this hypothesis, we maintained an experimental demography deer exclusion study for 6 y in a forest where the native ungulate Odocoileus virginianus (white-tailed deer) is overabundant and Alliaria petiolata (garlic mustard) is aggressively invading. Because population growth is multiplicative across time, we introduce new metrics to correctly integrate experimental effects across treatment years, the cumulative population growth rate, λc, and its geometric mean, λper-year, the time-averaged annual population growth rate. We determined λc and λper-year of the invader and of a common native, Trillium erectum. Our results conclusively demonstrate that deer are required for the success of Alliaria; its projected population trajectory ...