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/).

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).

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.

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.

Endangered species that exist in small isolated populations are at elevated risk of losing adaptive variation due to genetic drift. Analyses that estimate short-term effective population sizes, characterize historical demographic processes, and project the trajectory of genetic variation into the future are useful for predicting how levels of genetic diversity may change. Here, we use data from two independent types of genetic markers (single nucleotide polymorphisms [SNPs] and microsatellites) to evaluate genetic diversity in 17 populations spanning the geographic range of the endangered eastern massasauga rattlesnake (Sistrurus catenatus). First, we use SNP data to confirm previous reports that these populations exhibit high levels of genetic structure (overall Fst = 0.25). Second, we show that most populations have contemporary Ne estimates less than 50. Heterozygosity-fitness correlations in these populations provided no evidence for a genetic cost to living in small populations, though these tests may lack power. Third, model-based demographic analyses of individual populations indicate that all have experienced declines, with the onset of many of these declines occurring over timescales consistent with anthropogenic impacts (<200 years). Finally, forward simulations of the expected loss of variation in relatively large (Ne = 50) and small (Ne = 10) populations indicate they will lose a substantial amount of their current standing neutral variation (63% and 99%, respectively) over the next 100 years. Our results argue that drift has a significant and increasing impact on levels of genetic variation in isolated populations of this snake, and efforts to assess and mitigate associated impacts on adaptive variation should be components of the management of this endangered reptile.

BackgroundIn a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics.Methods and FindingsThe model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.ConclusionsWe present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.

Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: Ardisia escallonioides (Pascarella & Horvitz, 1998), Silene acaulis, and Bistorta vivipara (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.

This repository incorporates data and scripts associated with the paper "Biases in demographic modelling affect our understanding of recent divergence" published in Molecular Biology and Evolution (doi: 10.1093/molbev/msab047 ).

Included in the repository are the following compressed folders:

Alignment_and_SNP_Calling.zip. Contains bioinformatic pipelines to align raw data (available in GenBank from Bioproject PRJNA699405) and generate BAM and VCF files necessary for further analyses. Several VCF files are also included.

SFS.zip. Contains scripts to generate the 2d-SFS for demographic analyses of empirical data. Includes also input SNP files for moments.

pi_dxy.zip. This compressed folder contains a bash script to generate 1d-SFS and 2d-SFS in windows across the genome, and R scripts to derive summary statistics from those ((\pi), (\theta), (d_{xy}), Tajima's (D)) as well as the results (SFS and summary statistics in windows)

PCANGSD.zip. Contains arguments to generate the input file (in beagle format) for PCangsd, results from PCangsd (covariance matrix and selection test statistics) and a R script to plot a PCA.

Stairway_Plots.zip. Contains blueprint files to run StairwayPlots and results.

Simulations.zip. Contains commands to run all simulations, dadi and moments models for inference, results and some R scripts to reproduce plots.

Moments_models.zip. Contains the one-population and two-population models for moments that we designed for this study.

Moments_results.zip. Contains results from demographic models optimized for the empirical data, as well as scripts and results from Likelihood Ratio Tests and estimation of uncertainties using the Godambe Information Matrix.

Please note that most of these scripts and models are also available from GitHub (https://github.com/Nopaoli/Demographic-Modelling) .

Phrynosoma mcallii (flat-tailed horned lizards) is a species of conservation concern in the Colorado Desert of the United States and Mexico. We analyzed ddRADseq data from 45 lizards to estimate population structure, infer phylogeny, identify migration barriers, map genetic diversity hotspots, and model demography. We identified the Colorado River as the main geographic feature contributing to population structure, with the populations west of this barrier further subdivided by the Salton Sea. Phylogenetic analysis confirms that northwestern populations are nested within southeastern populations. The best-fit demographic model indicates Pleistocene divergence across the Colorado River, with significant bidirectional gene flow, and a severe Holocene population bottleneck. These patterns suggest that management strategies should focus on maintaining genetic diversity on both sides of the Colorado River and Salton Sea. We recommend additional lands in the U.S. and Mexico that should be con..., A ddRADseq dataset was collected for 45 lizards (including outgroups). Sequencing occurred on an Illumina NextSeq. Raw sequence data were processed (including mapping to the P. platyrhinos reference genome) using iPyrad. After filtering with VCFtools, the data were analyzed with the software packages adegenet (DAPC), Admixture, splitstree, IQTree, EEMS, and moments. Inputs, outputs, jobscripts, and other metadata for these analyses are included in this data package. Full methods are detailed in the paper., , # Population genomics of flat-tailed horned lizards (Phrynosoma mcallii) informs conservation and management across a fragmented Colorado Desert landscape

https://doi.org/10.5061/dryad.5x69p8dbj

A ddRADseq dataset was collected for 45 lizards (including outgroups). Sequencing occurred on an Illumina NextSeq. FASTQ data were processed, including mapping to the P. platyrhinos reference genome, using iPyrad. After filtering with VCFtools, the data were analyzed with the software packages adegenet, Admixture, splitstree, IQTree2, EEMS, and moments. Inputs, outputs, intermediate files, jobscripts, and other metadata for these analyses are included in this data package. Methods are detailed in the paper. Questions are welcome, please contact the corresponding author at gottschoa@si.edu.

Description of the data and file structure

There are nine zipped files which unpack to the following directories.

**adegenet.zi...

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.

Data and JAGS code to run base and sensitivity integrated population models using program R. Please see attached Metadata files for specific details.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).

More specifically, it contains:

- in `other_pop_data` folder files from World Bank, the International Database from the US Census, and from IHME

- in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace

- in the `UN` folder, the demographic projections from UN

- `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases

- `RFF.csv`, an overview file containing the population projections obtained by Resources For the Future

'- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6

This data in intended to be downloaded for use together with the package downloadable here.

The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

This is momi2 code for demographic modeling in Bronze Age Northern Eurasian Genetics in the Context of Development of Metallurgy and Siberian Ancestry

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.

README: The following supplementary material, methods, and data accompany the manuscript below which has been submitted for peer review at Systematic Biology

Title: Topology Testing and Demographic Modeling Illuminate a Novel Speciation Pathway in the Greater Caribbean Sea Following the Formation of the Isthmus of Panama. Authors: Benjamin M. Titus, H. Lisle Gibbs, Nuno Simões, Marymegan Daly

Supplemental Methods, Tables S1 and S3-S7, Figures S1: Titus_etal_Aped_SysBiol_SuppMat.docx Supplemental TableS2: TableS2_Aped_ddRADseq_stats.xlsx

Datasets:

AP_COI_all_wOutgroups.fasta - This file contains the aligned DNA barcode sequence data (mitochondrial cytochrome oxidase subunit I- COI) used for single locus species delimitation analyses for Ancylomenes pedersoni throughout the entire Tropical Western Atlantic, including outgroup taxa.

Apedersoni_all_samples_Dataset1_SystBiol.str- This file contains the Structure-formatted double digest Restriction-Site Asso...

Wildlife managers translocate greater sage-grouse (Centrocercus urophasianus; sage-grouse) to augment small populations, but translocated sage-grouse often fail to reproduce post-release, sometimes hampering conservation objectives. We performed two distinct sage-grouse translocation projects in California and North Dakota from 2017-2020 and employed two translocation methods at both sites: an established method of translocating females prior to the nesting season (i.e., a pre-nesting translocation), and a novel method wherein females were translocated with chicks after successfully hatching a nest in the source population (i.e., a brood translocation). Using an integrated population model (IPM), we estimated recruitment by females translocated with each method. We also estimated the finite rate of change in abundance in recipient and source populations that underwent brood and pre-nesting translocations to evaluate each method using a cost-benefit metric.

Understanding the origin of new species is a central goal in evolutionary biology. Diverging lineages often evolve highly heterogeneous patterns of genetic differentiation; however, the underlying mechanisms are not well understood. We investigated evolutionary processes governing genetic differentiation between the hybridizing campions Silene dioica (L.) Clairv. and S. latifolia Poiret. Demographic modeling indicated that the two species diverged with gene flow. The best-supported scenario with heterogeneity in both migration rate and effective population size suggested that a small proportion of the loci evolved without gene flow. Differentiation (FST) and sequence divergence (dXY) were correlated and both tended to peak in the middle of most linkage groups, consistent with reduced gene flow at highly differentiated loci. Highly differentiated loci further exhibited signatures of selection. In between-species population pairs, isolation by distance was stronger for genomic regions wit...

Demographic stochasticity can have large effects on the dynamics of small populations as well as on the persistence of rare genotypes and lineages. Survival is sensibly modeled as a binomial process, but annual reproductive success (ARS) is more complex and general models for demographic stochasticity do not exist. Here we introduce a stochastic model framework for ARS and illustrate some of its properties. We model a sequence of stochastic events: nest completion, the number of eggs or neonates produced, nest predation, and the survival of individual offspring to independence. We also allow multiple nesting attempts within a breeding season. Most of these components can be described by Bernoulli or binomial processes; the exception is the distribution of offspring number. Using clutch and litter size distributions from 53 vertebrate species, we demonstrate that among†individual variability in offspring number can usually be described by the generalized Poisson distribution. Our model f...

Uncovering the historical and contemporary processes shaping rare species with complex distributions is of growing importance due to threats such as habitat destruction and climate change. Species restricted to specialized, patchy habitat may persist by virtue of life history characteristics facilitating ongoing gene flow and dispersal, but they could also reflect the remnants of formerly widespread, suitable habitat that existed during past climate regimes. If formerly widespread species did not rely upon traits facilitating high dispersibility to persist, contemporary populations could be at high risk of extirpation or extinction. Fortunately, genomic investigations provide an opportunity to illuminate such alternative scenarios while simultaneously offering guidance for future management interventions. Herein, we test the role of these mechanisms in shaping patterns of genomic diversity and differentiation across a highly restricted and rare ecosystem: desert hanging gardens. We focus on Carex specuicola (Cyperaceae), a hanging garden obligate narrowly distributed in the Four Corners region of the southwestern United States that is listed as Threatened under the United States Endangered Species Act. Population structure and diversity analyses reveal that hanging garden populations are shaped by strong genetic drift, but that individuals in gardens are occasionally more closely related to individuals at other gardens than to individuals within the same garden. Similarly, gardens separated by long geographic distances may contain individuals that are more closely related compared to individuals in gardens separated by short geographic distances. Demographic modeling supports historical gene flow between some contemporary garden pairs, which is corroborated by low estimates of inbreeding coefficients and recent divergence times. As such, multiple lines of evidence support dispersal and gene flow across C. specuicola populations at both small and large spatial scales, indicating that even if C. specuicola was formerly more widespread, it may be well suited to persist in hanging gardens so long as suitable habitat remains available. Analyses like those demonstrated herein may be broadly applicable for understanding the short- and long-term evolutionary processes influencing rare species, and especially those having complex distributions across heterogeneous landscapes.