AIC calculated using -2(log) +2k, (where k is the number of free parameters in the model). Models in bold as in table 5. Based on AIC score the model (ABADE) provides the best fit to our data. This model had equal population sizes between the Negro and ancestral populations and unequal gene flow between Negro and Chubut systems (Table 5). Estimates of the time of divergence (years before present [to the nearest thousand]) between river systems were obtained by dividing t by the geometric mean of the mutation rate across loci ( = 4.008839×10−5: see methods for further details).

Data description for Rosa et al. - Frankenstein matrices: among-population life history variation affects the reliability and predictions of demographic models - Submitted to Journal of Animal Ecology - December 2023NOTE: these data are private and only intended for peer review. The repository will be made publicly available after acceptance.1) File "Frank_matrices_code.txt" contains the R code used to carry out matrix analyses and obtain "hybrid matrices".2) File "All_estimates.txt" contains the demographic parameters, estimated using program MARK, inputted in the R code to perform population matrix analysis.3)File "Appendix_2.xlsx" stores the data obtained during the analysis of the reversal of elasticities, performed for each of the three scenarios.

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population-level responses to environmental variation. Expanding on current methods, we 1) apply data-driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changes in seed-bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire-return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.

In the past three decades, the role of matrix-based demographic models in plant conservation has steadily increased. However, the reliability of these methods remains hotly debated. Most tests of model performance have relied on strict conditions for either the data sets being tested or the criteria used to judge accuracy of the results. This leads to a potential disconnect between the variety of ways in which models are used in practice and the limited set of conditions where their performance has been evaluated. As part of our working group, we set out to introduce and apply the idea that relevant tests depend on how exactly matrix models are used for managing populations. To this end, we systematically assessed 397 matrix models for plant populations to determine which population metrics (e.g. population growth rate, sensitivity, extinction risk) are being most commonly used in the literature and how literally authors are interpreting these metrics as predictions. The data sets available here contain both the citation information for all of the plant studies that we identified and the results of our review (see Crone et al., In Review, Ecology Letters). We have attempted to provide a nearly complete census of the available literature from 1966 through April 2009.

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.

Population q1 and q2 refer to the Negro and Chubut river systems respectively, whereas qa is the ancestral population for the given model. Migration estimates are for gene flow from Negro into the Chubut system (m1) whereas m2 is the reverse direction. Gene flow estimates of 0.001 are effectively zero. Values for t and the five demographic parameters are the high points of the posterior distribution. Five models (in bold) could not be rejected at α = 0.05 or using a Bonferroni corrected α = 0.003125. Time of divergence (t) is not included in the model selection.

Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated Population Models (IPMs) offer a possibility to estimate contributions of additional demographic parameters, for which no data have been explicitly collected: typically immigration. Such parametersare often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated.

To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using Northern Wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration, and sample size. We also used empirical data...

Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species’ population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies. We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking simple assumptions, that different demography can completely revert conclusions reached by a comparison based on an experiment focusing on a single life stage. We show that a "demographic correction", namely translating observed effects into differences in outcomes of demographic models, is a solution to this problem. It requires turning the detected effects from the experiment into changes of transition probabilities of projection matrix models. Although such solution is limited by the low number of species with demographic data available, we believe that existing data (and data likely to be collected in the near future) permit at least approximate handling of this problem.

Model-based analyses are common in phylogeographic inference because they parameterize processes such as population division, gene flow and expansion that are of interest to biologists. Approximate Bayesian Computation is a model-based approach that can be customized to any empirical system and used to calculate the relative posterior probability of several models, provided that suitable models can be identified for comparison. The question of how to identify suitable models is explored using data from Plethodon idahoensis, a salamander that inhabits the North American inland northwest temperate rainforest. First, we conduct an ABC analysis using five models suggested by previous research, calculate the relative posterior probabilities, and find that a simple model of population isolation has the best fit to the data (PP = 0.70). In contrast to this subjective choice of models to include in the analysis, we also specify models in a more objective manner by simulating prior distributions...

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust est..., The simulation code was written for this manuscript and the results were generated by running the code on the Massachusetts Green High Performance Computing Cluster. Kinship data for lemon sharks comes from: Feldheim, K. A., S. H. Gruber, J. D. DiBattista, E. A. Babcock, S. T. Kessel, A. P. Hendry, E. K. Pikitch, M. V. Ashley, and D. D. Chapman. 2014. Two decades of genetic profiling yields first evidence of natal philopatry and long-term fidelity to parturition sites in sharks. Molecular Ecology 23:110–117 and can be accessed at: https://doi.org/10.5061/dryad.1q9r8., , # Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments

This compilation of data and code contains three primary folders that can be accessed from the main directory:

1. Data_used_in_MS
2. JAGS_models
3. Scripts_used_in_MS
4. Simulation_log_key

Each of these folders/files and the structure of subfolders is described in details below.

Description of the data and file structure

• Data_used_in_MS: This folder contains the final data that are presented in the manuscript (MS), as well as the code used to analyze it. The structure and content of this directory are as follows:

  • 04_DataViz: This folder contains two primary scripts, as well as several functions that are sourced. The code assumes that the working directory is set to the same directory as the script, and that all folders and files from the repository are present.

    • mcmc analysis.R contains code for examining convergence...

Best fit demographic models for each model class are shown in bold, competing models within 2 log likelihood units of the maximum likelihood model are given below. N0 , NB and NA refer to current, during bottleneck and before bottleneck effective population sizes, while TA and TB refer to time in 2N0 generations to the change from NB to N0 and from NA to NB , respectively. SE stands for standard error while LRT stands for likelihood-ratio test.

An interactive Story Map Series℠ explaining the links between the Demographic Transition Model and population pyramids (population structure) for almost all the countries in the world. It provides an excellent way to make spatial links with the demographic data. For example, each country is mapped using an interactive symbol representing its stage on the DTM. On clicking the symbol for any country, a pop-up provides a statement about its stage on the DTM and its 2018 population pyramid, provided by PopulationPyramid.net.The tabs in the Story Map Series℠ take the reader or presenter through an introduction and explanation of the DTM, followed by detail about particular places / countries currently at each stage including an example of anomalies which are less consistent with the model.The story map will be useful for a wide range of students and teachers of geography, demography and development at secondary and tertiary level.Credits and further study*Story Map template by Esri*Demographic Transition video by GeographyAllTheWay*Population structure diagrams from PopulationPyramid.net by Martin de Wulf based in Brussels, Belgium.*DTM diagram and population pyramid icons from Cool Geography *Population Education / PopEdBlog*BBC Bitesize Population growth and change*Thanks also to Ed Morgan of the ONS for very helpful feedback and further information.NB The DTM stages for each country are estimated and may be altered in due course.

It is important to understand metapopulation dynamics and underlying demographic processes in heterogeneous landscapes. Traditionally demographic parameters are estimated using capture-recapture data that can be difficult to collect. Spatially explicit dynamic N-mixture models allow inference for demographic parameters, including dispersal, using count data of unmarked animals, but these models have only been shown effective under constant demographic parameters and dispersal between adjacent local populations.

In this study I aimed to compensate the weakness of spatially explicit dynamic N-mixtures and multistate capture-recapture models by jointly analyzing count and capture-recapture data. This spatially explicit integrated population model allows for spatiotemporal variation of demographic parameters in relation to environmental and density covariates and dispersal between any local populations. I conducted simulations to evaluate this model (1) for species with distinct life his...

This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

Alternatives in ecosystem-based management often differ with respect to trade-offs between ecosystem values. Ecosystem or food-web models and demographic models are typically employed to evaluate alternatives, but the approaches are rarely integrated to uncover conflicts between values. We applied multi-state models to a capture-recapture dataset on common guillemots Uria aalge breeding in the Baltic Sea to identify factors influencing survival. The estimated relationships were employed together with Ecopath-with-Ecosim food-web model simulations to project guillemot survival under six future scenarios incorporating climate change. The scenarios were based on management alternatives for eutrophication and cod fisheries, issues considered top priority for regional management, but without known direct effects on the guillemot population. Our demographic models identified prey quantity (abundance and biomass of sprat Sprattus sprattus) as the main factor influencing guillemot survival. Most scenarios resulted in projections of increased survival, in the near (2016-2040) and distant (2060-2085) future. However, in the scenario of reduced nutrient input and precautionary cod fishing, guillemot survival was projected to be lower in both future periods due to lower sprat stocks. Matrix population models suggested a substantial decline of the guillemot population in the near future, 24% per 10 years; and a smaller reduction, 1.1% per 10 years, in the distant future. To date, many stakeholders and Baltic Sea governments have supported reduced nutrient input and precautionary cod fishing and implementation is underway. Negative effects on non-focal-species have previously not been uncovered, but our results show that the scenario is likely to negatively impact the guillemot population. Linking model results allowed identifying trade-offs associated with management alternatives. This information is critical to thorough evaluation by decision-makers, but not easily obtained by food-web models or demographic models in isolation. Appropriate datasets are often available, making it feasible to apply a linked approach for better-informed decisions in ecosystem-based management.

basic R, JAGS, capture-recapture knowledge

Please see the Data_archive_README.rtf file for a description of the data.

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

In any population, some individuals make it big: they are among the few that produce many offspring, grow to large size, and so on. What distinguishes the lives of these happy few? We present three approaches for identifying what factors distinguish those "lucky" individuals who come to dominate reproduction in a population without fixed differences between individuals (genotype, site quality, etc.): comparing life-history trajectories for lucky and unlucky individuals and calculating the elasticity of the probability of becoming lucky to perturbations in demographic rates at a given size or a given age. As examples we consider published size-structured integral projection models for the tropical tree Dacrydium elatum and the semiarid shrub Artemisia ordosica and an age-size-structured matrix model for the tropical tree Cedrela ordosica. We find that good fortune (e.g., rapid growth) when small and young matters much more than good fortune when older and larger. Becoming lucky is primarily a matter of surviving while others die. For species with more variable growth (such as Cedrela and Ordosica), it is also a matter of growing fast. We focus on reproductive skew, but our methods are broadly applicable and can be used to investigate how individuals come to be exceptional in any aspect.