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opencc-zeroDec 2015View details →

Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models

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.

opencc-zeroDec 2013View details →

Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests

FST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to non-equilibrium demographic histories has been under-studied. We developed a landscape-genetics simulator to test the effects of isolation by distance (IBD) and range expansion on FST outlier methods. We evaluated the two most commonly used methods for the identification of FST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary non-independence). Parameterization with a set of neutral loci ("neutral parameterization") always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely-used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary non-independence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives under balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical p-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published FST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially-divergent selection.

opencc-zeroDec 2017View details →

Data from: Demographic inferences after a range expansion can be biased: the test case of the blacktip reef shark (Carcharhinus melanopterus)

The evolutionary history of species is a dynamic process as they modify, expand and contract their spatial distributions over time. Range expansions (REs) occur through a series of founder events that are followed by migration among neighbouring demes. The process usually results in structured metapopulations and leaves a distinct signature in the genetic variability of species. Explicitly modeling the consequences of complex demographic events such as REs is computationally very intensive. Here we propose an an alternative approach that requires less computational effort than a comprehensive RE model, but that can recover the demography of species undergoing a RE, by combining spatially explicit modelling with simplified but realistic metapopulation models. We examine the demographic and colonization history of Carcharhinus melanopterus, an abundant reef-associated shark, as a test case. We first used a population genomics approach to statistically confirm the occurrence of a RE in C. melanopterus and identify its origin in the Indo-Australian Archipelago. Spatial genetic modelling identified two waves of stepping-stone colonization: an eastward wave moving through the Pacific and a westward one moving through the Indian Ocean. We show that metapopulation models best describe the demographic history of this species and that not accounting for this may lead to incorrectly interpreting the observed genetic variation as signals of widespread population bottlenecks. Our study highlights insights that can be gained about demography by coupling metapopulation models with spatial modeling and underscores the need for cautious interpretation of population genetic data when advancing conservation priorities.

opencc-zeroDec 2017View details →

Data from: Genetic signatures of small effective population sizes and demographic declines in an endangered rattlesnake, Sistrurus catenatus

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.

Figure 7 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 7 - Numbers of the 10 most abundantly collected carabid species in reedbeds with regards to migrants (A) and residents only (B). Dominant species are in bold text, combined data for 2006/07; N (ex.) – number of specimens (after Makarov and Matalin 2009).

Figure 6 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 6 - Species diversity and the share of labile/stable components in particular habitats in the Lake Elton region, combined data for 2006/07 (black bars – labile component, white bars – stable component, line – number of species; N (sp.) – number of species).

Figure 4 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 4 - Seasonal dynamics of activity, as well as the age structure of the populations of Harpalus rufipes from reedbeds along the River Khara (A) and Pseudotaphoxenus rufitarsis major from the lakeside salt-marsh (B), in 2006 (see Figure 2 for further explanations).

Figure 3 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 3 - Seasonal dynamics of activity, as well as the age structure of the populations of Calathus ambiguus from grass-forb steppe with Amygdalus nana (A) and Pseudotaphoxenus rufitarsis major from sagebrush-grassland desert steppe on the northern slope of Ulagan Mountain (B), in 2006 (breaks in the periods of activity of immature specimens correspond to the time of aestivation parapause; see Figure 2 for further explanations).

Figure 2 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 2 - Seasonal dynamics of activity, as well as the age structure of the populations of Pogonus transfuga (A) and Brachinus hamatus (B) from reedbeds along the River Khara, combined data for 2006/07 (T – teneral, Im – immature, M – mature, Sp – spent beetles; solid lines below graphs parental generation, dashed lines below graphs – new generation; N (ex.) – number of specimens; 1, 2, 3 – first, second and third ten-day periods per month, respectively).

Figure 1 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 1 - Chronology of changes in periods of activity of individual 'age' groups, characterised by female gonad condition, in 'spring' (A) and 'autumn' (B) breeding carabid beetles (T – teneral, Im – immature, M – mature, Sp – spent beetles).

Figure 5 from: Matalin A, Makarov M (2011) Using demographic data to better interpret pitfall trap catches. ZooKeys 100: 223-254. https://doi.org/10.3897/zookeys.100.1530

Figure 5 - Seasonal variation in abundance curves and reproduction aspects in four resident carabid species coupled with abundance of a migrant-species Harpalus rufipes from reedbeds, combined data for 2006/07 (R and L right and left Y axis, respectively; N (ex.) – number of specimens).

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov28/100

Demographic Data and Clinical Presentation of Pediatric Uveitis

ClinicalTrials.gov study NCT06920017. IPD Sharing: Not stated. Countries: 0. Publications: 9.

publicOct 2017View details →

Data from: Demographic costs associated with differences in habitat space occupancy

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publicDec 2020View details →

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

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publicJun 2013View details →

Data from: Population genomics shed light on the demographic and adaptive histories of European invasion in the Pacific oyster, Crassostrea gigas

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