Demographic response of plant populations to habitat fragmentation and temporal environmental variability

Many plant species currently exist in fragmented populations of different sizes, while they also experience unpredictable climatic fluctuation over time. However, we still understand little about how plant demography responds to such spatial and temporal environmental variability. We studied population dynamics of an understory perennial herb Trillium camschatcense in the Tokachi plain of Hokkaido, Japan, where a significant effect of forest fragmentation on seedling recruitment was previously reported. Four populations across a range of fragment sizes were studied for 6 years, and the data were analyzed using matrix population models. Per capita fecundity (the number of recruits per plant) varied greatly among populations, but the variation in population growth rates (λ) was mainly driven by the variation in stasis and growth rates, suggesting that the general trend of reduced fecundity in fragmented populations may not be readily translated into subsequent dynamics. Temporal variation in λ among years was more than 2 times larger than spatial variation among populations, and this result was likely attributable to the contrasting response of correlation structures among demographic rates. The among-population variation in λ was dampened by negative covariation between matrix elements possibly due to density-dependent regulation as well as an inherent constraint that some elements are not independent, whereas positive covariation between matrix elements resulted in large temporal variation in λ. Our results show that population dynamics responded differently to habitat fragmentation and temporal variability of the environment, emphasizing the need to discriminate these spatial and temporal variations in demographic models. Although no populations were projected to be declining in stochastic simulations, correlation between current habitat size and plant density implies historical λ is positively related to habitat size.     

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, P_q(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 P_q(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.     

Inferences about population dynamics from count data using multistate models: a comparison to capture–recapture approaches

Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N‐mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N‐mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state‐specific count data, we show how our model can be used to estimate local population abundance, as well as density‐dependent recruitment rates and state‐specific survival. We apply our model to a population of black‐throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density‐dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.     

Land use heterogeneity causes variation in demographic viability of a bioindicator of species-richness in protected fen grasslands

Land use change is one of the main drivers of species extinction. In Europe, grasslands are under active conflict between conservation efforts and increasing agricultural pressures. Here, we examine the demographic effects of differential land use on the herbaceous perennial Trollius europaeus L. (Ranunculaceae), a bioindicator of species-richness and ecosystem services in wet grasslands of Central Europe. Demographic data were collected in 2006–2009 from nine populations in seven protected sites of northeastern Germany representing four land use types. We constructed stage-based matrix population models to explore the effects of various land management on demographic viability of focal populations. We show that most studied populations are declining (λ < 1), although the estimates of local extinction vary between ≤15 years for grazed and woodland populations, and 20–99 years for mown and abandoned populations. The joint information from our elasticity analyses and life table response experiments revealed that reproduction, growth of small vegetative individuals and survival of reproductive stages are most important for population viability. Our study shows that the current land uses in protected areas where T. europaeus is found is incompatible with its long-term viability. We suggest that, when compatible with in situ practices, grasslands containing this species be mown after maturity in order to enhance seedling recruitment and to reduce competition for juveniles. Prolonged extinction times in abandoned populations offer a buffer to develop conservation schemes there. An improvement of conservation measures is urgently needed to maintain the populations of this important bioindicator and its associated community of moist species-rich fen grasslands.     

Using genetic data to advance stream fish reintroduction science: a case study in brook trout

Widespread extirpation of native fish populations has led to a rise in species reintroduction efforts worldwide. Most efforts have relied on demographic data alone to guide project design and evaluate success. However, the genetic characteristics of many imperiled fish populations including low diversity, local adaptation, and hatchery introgression emphasize the importance of genetic data in the design and monitoring of reintroduction efforts. Focusing on a case study of brook trout (Salvelinus fontinalis) in North Carolina, we show how the combined use of genetic and demographic data can support reintroduction efforts by improving source population selection and providing opportunities to evaluate genetic viability and adaptive potential in restored populations. Using this combined approach, we reintroduced brook trout into a restored stream from two source populations and monitored changes in genetic diversity and population size in source and recipient populations. Three years after the initial translocation, the reintroduced population had comparable density, but higher genetic diversity, than either source population. This study demonstrates the utility of genetic and demographic data for reintroduction efforts, particularly when extant populations are genetically depauperate and maintaining adaptive potential is a primary restoration goal. However, we emphasize the value of continued monitoring at longer temporal and spatial scales to determine the effects of stochastic process on the long-term adaptive capacity and persistence of reintroduced populations. Overall, inclusion of genetic data in reintroduction efforts offers increased ability to meet project goals while simultaneously conserving critical sources of adaptive variation that exist across the landscape.     

The use of agent-based modelling of genetics in conservation genetics studies

Animal Landscape and Man Simulation System a genetically explicit agent-based model was used to obtain measures for the genetic and demographic status of simulated populations. This investigation aimed to test the applicability of this approach for assessing the effect of environmental perturbations on populations’ temporal and spatial dynamics. This was achieved by assessing how three simple scenarios with increasing degree of environmental disturbance, simulated by populations bottlenecks repeated at different intervals, affected the genetic and demographic characteristics of the simulated population. Model outputs from a simplified landscape scenario concurred with theoretical expectations validating the model in a qualitative way. Differences in medians, means and coefficient of variation of the observed (H_o) and expected heterozygosity (H_e), population census size (N), effective population size (N_e), inbreeding coefficient (F) and N_e/N ratio were observed for simulated populations. Impacts occurred rapidly after simulated bottleneck events and genetic estimates were less variable, and therefore more reliable, than demographic estimates. Precise genetic consequences of the bottlenecks repeated at different intervals, and resulting population perturbations, are a complex balance between effects on population sub-structure, size and founding events. Agent-based models are appropriate tools to simulate these interactions, being sufficiently flexible to mimic real population processes under a range of environmental conditions. Such models incorporating explicit genetics provide a promising new approach to evaluate the impact of environmental changes on genetic composition of populations.     

Integral projection models perform better for small demographic data sets than matrix population models: a case study of two perennial herbs

1. Matrix population models are widely used to describe population dynamics, conduct population viability analyses and derive management recommendations for plant populations. For endangered or invasive species, management decisions are often based on small demographic data sets. Hence, there is a need for population models which accurately assess population performance from such small data sets.
2. We used demographic data on two perennial herbs with different life histories to compare the accuracy and precision of the traditional matrix population model and the recently developed integral projection model (IPM) in relation to the amount of data.
3. For large data sets both matrix models and IPMs produced identical estimates of population growth rate (λ). However, for small data sets containing fewer than 300 individuals, IPMs often produced smaller bias and variance for λ than matrix models despite different matrix structures and sampling techniques used to construct the matrix population models.
4. Synthesis and applications . Our results suggest that the smaller bias and variance of λ estimates make IPMs preferable to matrix population models for small demographic data sets with a few hundred individuals. These results are likely to be applicable to a wide range of herbaceous, perennial plant species where demographic fate can be modelled as a function of a continuous state variable such as size. We recommend the use of IPMs to assess population performance and management strategies particularly for endangered or invasive perennial herbs where little demographic data are available.     

Variation in demographic and fine-scale genetic structure with population-history stage of <Emphasis Type="Italic">Hemerocallis taeanensis</Emphasis> (Liliaceae) across the landscape

Fine-scale genetic structure (FSGS) in plant populations is expected to be influenced by variation in demographic processes across space and over time. I chose Hemerocallis taeanensis (Liliaceae), a perennial herb with a rapid population turnover, to quantify how demographic structure and FSGS change with a population’s history (i.e., density). Nonaccumulative O-ring statistic and spatial autocorrelation analysis (kinship coefficient, F _ij ) were used to quantify spatial patterns of individuals and FSGS in four populations belonging to two population stages (expansion and maturation) in west-central Korea. The O-ring function revealed that significant aggregation of individuals occurs at short spatial scales during the earlier stage of population expansion, which reflects restricted seed dispersal around maternal individuals. However, this pattern disappears as the population density increases during population maturation, probably due to a high population density. Significant evidence of FSGS was found in two populations at the stage of population expansion (Sp, a statistic which describes the rate of decrease of pairwise kinship with distance, was 0.018 and 0.029). The results show that most seeds fall around maternal plants when initially established colonists proliferate at suitable microhabitats. In contrast to this, much lower Sp values (−0.003 and 0.004) were estimated for two populations at the stage of population maturation, which may result from the overlapping of seed shadows due to high adult density. All of these results demonstrate considerable variation in within-population demographic and genetic structures of H. taeanensis with respect to population temporal stage across the landscape.     

Dynamics of isolated <Emphasis Type="Italic">Saponaria bellidifolia</Emphasis> Sm. populations at northern range periphery

Four populations of Saponaria bellidifolia situated at the species’ northern range periphery (Apuseni Mountains, southeastern Carpathians) were monitored over a period of 5 years. They were chosen to represent different habitat types (rocky, fixed screes, open screes and grassy), disturbance regime (fire), and population sizes (categorized as large and small). The reproductive effort was quantified, and matrix models were used to describe the population dynamics and to assess population viability. Saponaria bellidifolia had very stable population dynamics in the harsh and stable abiotic conditions of the outcrops where populations occur. Habitat conditions exerted a notable influence on the species’ population reproductive performance, growth rate, and vital rates, whereas population size and climate did not have a clear-cut effect on the dynamics of the species. Saponaria bellidifolia maintains viable populations in the southeastern Carpathians, at its northern range periphery.     

Effects of climate variability on the demography of wild geladas

Nonhuman primates are an essential part of tropical biodiversity and play key roles in many ecosystem functions, processes, and services. However, the impact of climate variability on nonhuman primates, whether anthropogenic or otherwise, remains poorly understood. In this study, we utilized age‐structured matrix population models to assess the population viability and demographic variability of a population of geladas (Theropithecus gelada) in the Simien Mountains, Ethiopia with the aim of revealing any underlying climatic influences. Using data from 2008 to 2019 we calculated annual, time‐averaged, and stochastic population growth rates (λ) and investigated relationships between vital rate variability and monthly cumulative rainfall and mean temperature. Our results showed that under the prevailing environmental conditions, the population will increase (λ _s = 1.021). Significant effects from rainfall and/or temperature variability were widely detected across vital rates; only the first year of infant survival and the individual years of juvenile survival were definitively unaffected. Generally, the higher temperature in the hot‐dry season led to lower survival and higher fecundity, while higher rainfall in the hot‐dry season led to increased survival and fecundity. Overall, these results provide evidence of greater effects of climate variability across a wider range of vital rates than those found in previous primate demography studies. This highlights that although primates have often shown substantial resilience to the direct effects of climate change, their vulnerability may vary with habitat type and across populations.     

Yearly variation in recruitment and its effect on population dynamics inYoldia notabilis (Mollusca: Bivalvia), analyzed using projection matrix model

Long-term variation in recruitment was estimated by constructing projection matrices for a marine bivalve, Yoldia notabilis, at two stations in Otsuchi Bay, northeastern Japan, and the effects of its variation on population dynamics were examined using a simple matrix model. The matrix model was developed from the Leslie matrix, in which the population growth rate λ was expressed as a function of recruitment rate r₀. The equilibrium recruitment rate r_s, or the recruitment rate required to maintain population at constant size (λ=1), was expressed by the reciprocal of the reproductive value of a newly recruited individual. The estimates of r_s for the field population were lower at the shallower station than at the deeper station, reflecting higher survivorship and fecundity. Past recruitment rate estimated both by the field samplings for 3 years and by the back-calculation from the current age structure for over 10 years showed large yearly variation, ranging between 0 and 58.6×10⁻⁴. The estimates were larger than r_s, and hence, large enough to increase population size (λ>1) only in approximately one-third of the estimated years. This suggests that the population has been maintained by occasional successful recruitment occurring once every few years.     

A functional trait approach to identifying life history patterns in stochastic environments

Temporal variation in demographic processes can greatly impact population dynamics. Perturbations of statistical coefficients that describe demographic rates within matrix models have, for example, revealed that stochastic population growth rates (log(λ_s)) of fast life histories are more sensitive to temporal autocorrelation of environmental conditions than those of slow life histories. Yet, we know little about the mechanisms that drive such patterns. Here, we used a mechanistic, functional trait approach to examine the functional pathways by which a typical fast life history species, the macrodetrivore Orchestia gammarellus, and a typical slow life history species, the reef manta ray Manta alfredi, differ in their sensitivity to environmental autocorrelation if (a) growth and reproduction are described mechanistically by functional traits that adhere to the principle of energy conservation, and if (b) demographic variation is determined by temporal autocorrelation in food conditions. Opposite to previous findings, we found that O. gammarellus log(λ_s) was most sensitive to the frequency of good food conditions, likely because reproduction traits, which directly impact population growth, were most influential to log(λ_s). Manta alfredi log(λ_s) was instead most sensitive to temporal autocorrelation, likely because growth parameters, which impact population growth indirectly, were most influential to log(λ_s). This differential sensitivity to functional traits likely also explains why we found that O. gammarellus mean body size decreased (due to increased reproduction) but M. alfredi mean body size increased (due to increased individual growth) as food conditions became more favorable. Increasing demographic stochasticity under constant food conditions decreased O. gammarellus mean body size and increased log(λ_s) due to increased reproduction, whereas M. alfredi mean body and log(λ_s) decreased, likely due to decreased individual growth. Our findings signify the importance of integrating functional traits into demographic models as this provides mechanistic understanding of how environmental and demographic stochasticity affects population dynamics in stochastic environments.     

Evaluating methods for estimating local effective population size with and without migration

Effective population size is a fundamental parameter in population genetics, evolutionary biology, and conservation biology, yet its estimation can be fraught with difficulties. Several methods to estimate N_e from genetic data have been developed that take advantage of various approaches for inferring N_e. The ability of these methods to accurately estimate N_e, however, has not been comprehensively examined. In this study, we employ seven of the most cited methods for estimating N_e from genetic data (Colony2, CoNe, Estim, MLNe, ONeSAMP, TMVP, and NeEstimator including LDNe) across simulated datasets with populations experiencing migration or no migration. The simulated population demographies are an isolated population with no immigration, an island model metapopulation with a sink population receiving immigrants, and an isolation by distance stepping stone model of populations. We find considerable variance in performance of these methods, both within and across demographic scenarios, with some methods performing very poorly. The most accurate estimates of N_e can be obtained by using LDNe, MLNe, or TMVP; however each of these approaches is outperformed by another in a differing demographic scenario. Knowledge of the approximate demography of population as well as the availability of temporal data largely improves N_e estimates.     

Linking environmental and demographic data to predict future population viability of a perennial herb

Recent advances in stochastic demography provide tools to examine the importance of random and periodic variation in vital rates for population dynamics. In this study, we explore with simulations the effect of disturbance regime on population dynamics and viability. We collected 7 years of demographic data in three populations of the perennial herb Primula farinosa, and used these data to examine how variation in vital rates affected population viability parameters (stochastic growth rate, λ_S), and how vital rates were related to weather conditions. Elasticity analysis indicated that the stochastic growth rate was very sensitive to changes in regeneration, quantified as the production, survival, and germination of seeds. In one of the study years, all seedlings and mature plants in the demography plots died. This extinction coincided with the driest summer during the study period. Simulations suggested that a future increase in the frequency of high-mortality years due to climate change would result in reduced population growth rate, and an increased importance of survival in the seed bank for population viability. The results illustrate how the limited demographic data typically available for many natural systems can be used in simulation models to assess how environmental change will affect population viability.     

Integrating genetic data and population viability analyses for the identification of harbour seal (Phoca vitulina) populations and management units

Identification of populations and management units is an essential step in the study of natural systems. Still, there is limited consensus regarding how to define populations and management units, and whether genetic methods allow for inference at the relevant spatial and temporal scale. Here, we present a novel approach, integrating genetic, life history and demographic data to identify populations and management units in southern Scandinavian harbour seals. First, 15 microsatellite markers and model‐ and distance‐based genetic clustering methods were used to determine the population genetic structure in harbour seals. Second, we used harbour seal demographic and life history data to conduct population viability analyses (PVAs) in the vortex simulation model in order to determine whether the inferred genetic units could be classified as management units according to Lowe and Allendorf's (Molecular Ecology, 19, 2010, 3038) ‘population viability criterion’ for demographic independence. The genetic analyses revealed fine‐scale population structuring in southern Scandinavian harbour seals and pointed to the existence of several genetic units. The PVAs indicated that the census population size of each of these genetic units was sufficiently large for long‐term population viability, and hence that the units could be classified as demographically independent management units. Our study suggests that population genetic inference can offer the same degree of temporal and spatial resolution as ‘nongenetic’ methods and that the combined use of genetic data and PVAs constitutes a promising approach for delineating populations and management units.     

Inferring past demographic changes from contemporary genetic data: A simulation‐based evaluation of the ABC methods implemented in diyabc

Inferring the demographic history of species and their populations is crucial to understand their contemporary distribution, abundance and adaptations. The high computational overhead of likelihood‐based inference approaches severely restricts their applicability to large data sets or complex models. In response to these restrictions, approximate Bayesian computation (ABC) methods have been developed to infer the demographic past of populations and species. Here, we present the results of an evaluation of the ABC‐based approach implemented in the popular software package diyabc using simulated data sets (mitochondrial DNA sequences, microsatellite genotypes and single nucleotide polymorphisms). We simulated population genetic data under five different simple, single‐population models to assess the model recovery rates as well as the bias and error of the parameter estimates. The ability of diyabc to recover the correct model was relatively low (0.49): 0.6 for the simplest models and 0.3 for the more complex models. The recovery rate improved significantly when reducing the number of candidate models from five to three (from 0.57 to 0.71). Among the parameters of interest, the effective population size was estimated at a higher accuracy compared to the timing of events. Increased amounts of genetic data did not significantly improve the accuracy of the parameter estimates. Some gains in accuracy and decreases in error were observed for scaled parameters (e.g., N_eμ) compared to unscaled parameters (e.g., N_e and μ). We concluded that diyabc ‐based assessments are not suited to capture a detailed demographic history, but might be efficient at capturing simple, major demographic changes.     

Quantifying the contribution of immigration to population dynamics: a review of methods,evidence and perspectives in birds and mammals

The demography of a population is often reduced to the apparent (or local) survival of individuals and their realised fecundity within a study area defined according to logistical constraints rather than landscape features. Such demographics are then used to infer whether a local population contributes positively to population dynamics across a wider landscape context. Such a simplistic approach ignores a fundamental process underpinning population dynamics: dispersal. Indeed, it has long been accepted that immigration contributed by dispersers that emigrated from neighbouring populations may strongly influence the net growth of a local population. To date however, we lack a clear picture of how widely immigration rate varies both among and within populations, in relation to extrinsic and intrinsic ecological conditions, even for the best‐studied avian and mammalian populations. This empirical knowledge gap precludes the emergence of a sound conceptual framework that ought to inform conservation and population ecology. This review, conducted on both birds and mammals, has thus three complementary objectives. First, we describe and evaluate the relative merits of methods used to quantify immigration and how they relate to widely applicable metrics. We identify two simple and unifying metrics to measure immigration: the immigration rate i_t defined as the ratio of the number of immigrants present in the population at time t + 1 and the total breeding population in year t, and π_t , the proportion of immigrants among new recruits (i.e. new breeders). Two recently developed methods are likely to provide the most valuable data on immigration in the near future: individual parentage (rather than population) assignments based on genetic sampling, and spatially explicit integrated population models combining multiple sources of demographic data (survival, fecundity and population counts). Second, we report on a systematic literature review of studies providing a quantitative measure of immigration. Although the diversity of methods employed precludes detailed analyses, it appears that the number of immigrants exceeds locally born individuals in recruitment for most avian populations (median π_t  = 0.57, N = 45 estimates from 37 studies), a figure twofold higher than estimated for mammalian populations (median π_t  = 0.26, N = 33 estimates from 11 studies). Third, recent quantitative studies reveal that immigration can be the main driver of temporal variation in population growth rates, across a wide array of demographic and spatial contexts. To what extent immigration acts as a regulatory process has however been considered only rarely to date and deserves more attention. Overall, it is likely that most populations benefit from immigrants without necessarily being sink populations. Furthermore, we suggest that quantitative estimates of immigration should be core to future demographic studies and plead for more empirical evidence about the ways in which immigration interacts with local demographic processes to shape population dynamics. Finally, we discuss how to tackle spatial population dynamics by exploring, beyond the classical source–sink framework, the extent to which populations exchange individuals according to spatial scale and type of population distribution throughout the landscape.     

Effect of reproductive modes and environmental heterogeneity in the population dynamics of a geographically widespread clonal desert cactus

The dynamics of plant populations in arid environments are largely affected by the unpredictable environmental conditions and are fine-tuned by biotic factors, such as modes of recruitment. A single species must cope with both spatial and temporal heterogeneity that trigger pulses of sexual and clonal establishment throughout its distributional range. We studied two populations of the clonal, purple prickly pear cactus, Opuntia macrocentra, in order to contrast the factors responsible for the population dynamics of a common, widely distributed species. The study sites were located in protected areas that correspond to extreme latitudinal locations for this species within the Chihuahuan Desert. We studied both populations for four consecutive years and determined the demographic consequences of environmental variability and the mode of reproduction using matrix population models, life table response experiments (LTREs), and loop and perturbation analyses. Although both populations seemed fairly stable (population growth rate, λ∼1), different demographic parameters and different life cycle routes were responsible for this stability in each population. In the southernmost population (MBR) LTRE and loop and elasticity analyses showed that stasis is the demographic process with the highest contributions to λ, followed by sexual reproduction, and clonal propagation contributed the least. The northern population (CR) had both higher elasticities and larger contributions of stasis, followed by clonal propagation and sexual recruitment. Loop analysis also showed that individuals in CR have more paths to complete a life cycle than those in MBR. As a consequence, each population differed in life history traits (e.g., size class structure, size at sexual maturity, and reproductive value). Numerical perturbation analyses showed a small effect of the seed bank on the λ of both populations, while the transition from seeds to seedlings had an important effect mainly in the northern population. Clonal propagation (higher survival and higher contributions to vital rates) seems to be more important for maintaining populations over long time periods than sexual reproduction.     

Mating system parameters and population genetic structure in pioneer populations of Prosopis velutina (Leguminosae)

This study describes the genetic structure and provides estimates of mating system parameters in three natural populations of Prosopis velutina Woot. in southeastern Arizona. F statistics derived from isozyme data revealed the presence of both interpopulation and intrapopulation genetic differentiation. This population structure is discussed in relation to the ecological history of these populations that invaded the grasslands sites from adjacent riparian areas within the last 90 years, and possible modes of seed dispersal. The multilocus estimation program MLT of Ritland (Journal of Heredity 8: 235–237, 1990) was used to provide estimates of t_s, t_m, and the fixation index (F) of the maternal parents. Average estimates of t_s ranged from 0.591 to 0.912. Estimates of t_m ranged from 0.609 to 1.004, and averaged 0.758. The difference between t_m and t_s, which provides a measure of biparental inbreeding, averaged 0.058. This last result, together with significant heterogeneity found in pollen allele frequencies, suggests that a family structure has developed in the populations. A negative F value for one of the populations suggests selection against homozygotes between the seedling stage and maturity.     

A genetic demographic analysis of Lake Malawi rock‐dwelling cichlids using spatio‐temporal sampling

We estimated the effective population sizes (N_e) and tested for short‐term temporal demographic stability of populations of two Lake Malawi cichlids: Maylandia benetos, a micro‐endemic, and Maylandia zebra, a widespread species found across the lake. We sampled a total of 351 individuals, genotyped them at 13 microsatellite loci and sequenced their mitochondrial D‐loop to estimate genetic diversity, population structure, demographic history and effective population sizes. At the microsatellite loci, genetic diversity was high in all populations. Yet, genetic diversity was relatively low for the sequence data. Microsatellites yielded mean N_e estimates of 481 individuals (±99 SD) for M. benetos and between 597 (±106.3 SD) and 1524 (±483.9 SD) individuals for local populations of M. zebra. The microsatellite data indicated no deviations from mutation–drift equilibrium. Maylandia zebra was further found to be in migration–drift equilibrium. Temporal fluctuations in allele frequencies were limited across the sampling period for both species. Bayesian Skyline analyses suggested a recent expansion of M. zebra populations in line with lake‐level fluctuations, whereas the demographic history of M. benetos could only be estimated for the very recent past. Divergence time estimates placed the origin of M. benetos within the last 100 ka after the refilling of the lake and suggested that it split off the sympatric M. zebra population. Overall, our data indicate that micro‐endemics and populations in less favourable habitats have smaller N_e, indicating that drift may play an important role driving their divergence. Yet, despite small population sizes, high genetic variation can be maintained.