Fitness and evolution in clonal plants: the impact of clonal growth

Seeds have often been emphasized in estimates of plant fitness because they are the units that carry genes to the next generation, disperse, and found new populations. We contend that clonal growth also needs to be considered when estimating fitness in clonal plants, regardless of whether fitness is measured from a genet or ramet perspective. Clonal growth affects genet fitness through both genet persistence and seed production. It affects ramet fitness through new ramet production, because both seeds and clonal propagants are considered offspring. The differential production of clonal propagants will contribute to fitness differences among individuals which may result in population-level changes in allele frequencies (i.e. microevolution). We describe a form of selection unique to clonal organisms, genotypic selection, that can result in evolution. Genotypic selection occurs when genotypically based traits are associated with differences in the rate of ramet production. It can lead to evolutionary change in quantitative trait means both directly and indirectly. It leads directly to change in the ramet population by increasing the proportion of ramets with more advantageous trait values. From the genet perspective, it leads indirectly to evolution within and among populations whenever significant portions of the genetic effect on a trait are inherited through seed. We argue that under most conditions, clonal growth will play a major role in the microevolution of clonal plants.     

Longevity of clonal plants: why it matters and how to measure it

Background

Species'' life-history and population dynamics are strongly shaped by the longevity of individuals, but life span is one of the least accessible demographic traits, particularly in clonal plants. Continuous vegetative reproduction of genets enables persistence despite low or no sexual reproduction, affecting genet turnover rates and population stability. Therefore, the longevity of clonal plants is of considerable biological interest, but remains relatively poorly known.

Scope

Here, we critically review the present knowledge on the longevity of clonal plants and discuss its importance for population persistence. Direct life-span measurements such as growth-ring analysis in woody plants are relatively easy to take, although, for many clonal plants, these methods are not adequate due to the variable growth pattern of ramets and difficult genet identification. Recently, indirect methods have been introduced in which genet size and annual shoot increments are used to estimate genet age. These methods, often based on molecular techniques, allow the investigation of genet size and age structure of whole populations, a crucial issue for understanding their viability and persistence. However, indirect estimates of clonal longevity are impeded because the process of ageing in clonal plants is still poorly understood and because their size and age are not always well correlated. Alternative estimators for genet life span such as somatic mutations have recently been suggested.

Conclusions

Empirical knowledge on the longevity of clonal species has increased considerably in the last few years. Maximum age estimates are an indicator of population persistence, but are not sufficient to evaluate turnover rates and the ability of long-lived clonal plants to enhance community stability and ecosystem resilience. In order to understand the dynamics of populations it will be necessary to measure genet size and age structure, not only life spans of single individuals, and to use such data for modelling of genet dynamics.     

Sexual reproduction and clonal growth in Reinhardtia gracilis (Palmae), an understory tropical palm

Patterns of sexual reproduction and clonal growth were investigated in the understory palm Reinhardtia gracilis var. gracilior over a 3-yr period. R. gracilis is a very abundant clonal palm in the tropical rain forest of Los Tuxtlas, Veracruz, México. Because ramets form clumps, genets are easily identified in the field. Genets were monitored in a 0.5-ha area, and classified by size according to the number of ramets they possessed. In contrast to clonal growth, sexual reproduction was highly dependent on genet size. The probability of reproduction, the number of inflorescences, and the number of fruits produced were positively correlated with genet size. However, neither the probability of producing a ramet, nor the number of ramets produced per genet were correlated with genet size. Over the 3 yr of study, 55% of the genet population had at least one ramet with reproductive structures, while <1% (a single genet in one year) had six ramets with flowers. Thirty-two percent of the mature genets reproduced during each of three consecutive years. In contrast, 58% of the genets produced no new ramets during these 3 yr. No evidence was found of a trade-off between clonal growth and sexual reproduction. Ramet production increases genet size and this in turn increases genet reproductive performance. Clonal growth in this species may be viewed as a growth strategy that tends to maximize genet fitness.     

Sampling strategies for assessing the overall density of the cassava green mite (Mononychellus tanajoa)

Six different sampling methods to estimate the density of the cassava green mite, Mononychellus tanajoa, are categorized according to whether leaves or leaflets are used as secondary sampling units and whether the number of leaves on the sampled plants are enumerated, estimated from an independent plant sample, or not censused at all. In the last case, sampling can provide information only on the average number of mites per leaf and its variance, while information on stratum sizes is necessary to estimate the mean number of mites per plant as well. It is shown that leaflet-sampling is as reliable as leaf-sampling for the same number of sampling units. When stratum sizes are estimated from a separate plant sample, sampling time may also be reduced, but the estimated mean density and its variance may be biased if mite density and plant size are correlated. Sampling data show that the within-plant variance contributes relatively little to the overall variance of the population density estimates. It points at a sampling strategy in which the number of primary units (plants) is as large as possible at the expense of secondary units (leaflets) per plant. Mean-variance relationships may be applied to estimate sample variances and can be used even when only one leaflet is taken per plant per stratum. An unequal allocation of primary units among strata can increase precision, but the gain is small compared with an equal allocation. Leaf area can be predicted from the length of the longest leaflet and the number of leaflets.     

Genet structure and determinants of clonal structure in a temperate deciduous woodland herb, Uvularia perfoliata

1 We used isozyme variation to examine the genet structure of Uvularia perfoliata patches in gap and closed canopy habitats in a temperate deciduous forest in Maryland, USA.
2 A large patch in a gap habitat was composed of a small number of widely spread genets with many ramets, and a large number of genets with more restricted distribution and few ramets. Genets with many ramets were patchily distributed at a metre scale. Analysis of genet structure on a scale of square centimetres, however, revealed that the genets were highly intermingled with no clear boundaries between them. The presence at both scales of sampling of many genets with unique multilocus genotypes indicated continuing genet recruitment within the population.
3 In the closed canopy habitat, the patches examined were each composed of a single unique multilocus genotype, suggesting that each had developed by asexual propagation following the establishment of a single genet.
4 The clonal structure of U. perfoliata patches in both gap and closed canopy habitats therefore appears to depend on recruitment patterns of genets. Populations in closed canopy habitats are characterized by a 'waiting' strategy, in which asexual ramet production maintains populations until genet recruitment by seed production can occur under the more optimal conditions associated with canopy gaps. Extended sampling suggests that the genetic diversity of U. perfoliata populations is primarily controlled by the disturbance regime of the forest canopy.     

The oldest known clones of Salix herbacea growing in the Northern Apennines,Italy are at least 2000 years old

Premise

Dominant in many ecosystems around the world, clonal plants can reach considerable ages and sizes. Due to their modular growth patterns, individual clonal plants (genets) can consist of many subunits (ramets). Since single ramets do not reflect the actual age of genets, the ratio between genet size (radius) and longitudinal annual growth rate (LAGR) of living ramets is often used to approximate the age of clonal plants. However, information on how the LAGR changes along ramets and how LAGR variability may affect age estimates of genets is still limited.

Methods

We assessed the variability of LAGR based on wood-section position along the ramets and on the duration of the growing season on three genetically distinct genets of Salix herbacea growing in the Northern Apennines (Italy). We compared genet ages estimated by dividing genet radius by the LAGRs of its ramets.

Results

LAGR increased significantly from the stem apex to the root collar; indicating that ramet growth rate decreased with time. Furthermore, a difference of ca. 2 weeks in the onset of the growing period did not impact LAGR. Considering the high LAGR variability, we estimated that the three genets started to grow between ~2100 and ~7000 years ago, which makes them the oldest known clones of S. herbacea even considering the most conservative age estimate.

Conclusions

Our findings indicate that analyzing ramets at the root collar provides an integrative measurement of their overall LAGR, which is crucial for estimating the age of genets.     

Nonparametric Lower Bounds for Species Richness and Shared Species Richness under Sampling without Replacement

Summary A number of species richness estimators have been developed under the model that individuals (or sampling units) are sampled with replacement. However, if sampling is done without replacement so that no sampled unit can be repeatedly observed, then the traditional estimators for sampling with replacement tend to overestimate richness for relatively high-sampling fractions (ratio of sample size to the total number of sampling units) and do not converge to the true species richness when the sampling fraction approaches one. Based on abundance data or replicated incidence data, we propose a nonparametric lower bound for species richness in a single community and also a lower bound for the number of species shared by multiple communities. Our proposed lower bounds are derived under very general sampling models. They are universally valid for all types of species abundance distributions and species detection probabilities. For abundance data, individuals' detectabilities are allowed to be heterogeneous among species. For replicated incidence data, the selected sampling units (e.g., quadrats) need not be fully censused and species can be spatially aggregated. All bounds converge correctly to the true parameters when the sampling fraction approaches one. Real data sets are used for illustration. We also test the proposed bounds by using subsamples generated from large real surveys or censuses, and their performance is compared with that of some previous estimators.     

Out in the cold – how big and how old? Genetic fingerprinting reveals long‐lived individuals withstand climatic oscillations in the arctic‐alpine

In long‐lived, clonally reproducing species, assessing organism size is a nontrivial endeavour because each genetically distinct entity (genet) may comprise multiple modular units (ramets). Attributes of clonally reproducing populations, such as genet size, longevity and clonal diversity (the number of genets in a population), have significant implications for the persistence of populations over time. In the context of climate change, population persistence contributes to community stability and ecosystem resilience. Do clonal individuals persist through periods of climatic oscillations? Are clonal populations composed of a few large and persistent clones, or do they include clones of different sizes and ages? In this issue, de Witte et al. (2012) present an exciting analysis of clonal diversity and genet longevity in populations of four arctic‐alpine plant species with contrasting life histories: Carex curvula, Dryas octopetala, Salix herbacea and Vaccinium uliginosum. Using amplified fragment length polymorphism (AFLP) data, the authors demonstrate that genet size ranged from a few centimetres to 18 metres and age estimates for the largest genets ranged from 500 to 4900 years. These data reveal that clonally reproducing populations include individuals that have outlived significant changes in climate. Despite the longevity of some individuals, clonal diversity within populations was high, with most individuals existing as small, relatively young genets. Long‐lived individuals, together with high numbers of younger plants, ensure repeated recruitment and population persistence over time. This study represents a novel and timely contribution to a growing body of work aimed at understanding population persistence in changing climates.     

Spatial auto-correlation and auto-regressive models estimation from sample survey data

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Extensive simulation experiments based on two well-known data sets are used to assess the impact of the spatial sampling design, the auto-correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise.     

Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities

1. Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to traditional monitoring programs.
2. One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.
3. We used individual‐based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long‐lived mammal species subject to lethal sampling.
4. Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov–Smirnov tests.
5. Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.

     

Clonal genetic structure and diversity in populations of an aquatic plant with combined vs. separate sexes

Clonality is often implicated in models of the evolution of dioecy, but few studies have explicitly compared clonal structure between plant sexual systems, or between the sexes in dioecious populations. Here, we exploit the occurrence of monoecy and dioecy in clonal Sagittaria latifola (Alismataceae) to evaluate two main hypotheses: (i) clone sizes are smaller in monoecious than dioecious populations, because of constraints imposed on clone size by costs associated with geitonogamy; (ii) in dioecious populations, male clones are larger and flower more often than female clones because of sex‐differential reproductive costs. Differences in clone size and flowering could result in discordance between ramet‐ and genet‐based sex ratios. We used spatially explicit sampling to address these hypotheses in 10 monoecious and 11 dioecious populations of S. latifolia at the northern range limit in Eastern North America. In contrast to our predictions, monoecious clones were significantly larger than dioecious clones, probably due to their higher rates of vegetative growth and corm production, and in dioecious populations, there was no difference in clone size between females and males; ramet‐ and genet‐based sex ratios were therefore highly correlated. Genotypic diversity declined with latitude for both sexual systems, but monoecious populations exhibited lower genotypic richness. Differences in life history between the sexual systems of S. latifolia appear to be the most important determinants of clonal structure and diversity.     

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.     

Density and genetic structure of black bears in coastal South Carolina

The frequency of black bear (Ursus americanus) sightings, vehicle collisions, and nuisance incidents in the coastal region of South Carolina has increased over the past 4 decades. To develop the statewide Black Bear Management and Conservation Strategy, the South Carolina Department of Natural Resources needed reliable information for the coastal population. Because no such data were available, we initiated a study to determine population density and genetic structure of black bears. We selected 2 study areas that were representative of the major habitat types in the study region: Lewis Ocean Bay consisted primarily of Carolina Bays and pocosin habitats, whereas Carvers Bay was representative of extensive pine plantations commonly found in the region. We established hair snares on both study areas to obtain DNA from hair samples during 8 weekly sampling periods in 2008 and again in 2009. We used genotypes to obtain capture histories of sampled bears. We estimated density using spatially explicit capture–recapture (SECR) models and used information-theoretic procedures to fit parameters for capture heterogeneity and behavioral responses and to test if density and model parameters varied by year. Model-averaged density was 0.046 bears/km² (SE = 0.011) for Carvers Bay and 0.339 bears/km² (SE = 0.056) for Lewis Ocean Bay. Next, we sampled habitat covariates for all locations in the SECR sampling grid to derive spatially explicit estimates of density based on habitat characteristics. Addition of habitat covariates had substantial support, and accounted for differences in density between Carvers Bay and Lewis Ocean Bay; black bear density showed a negative association with the area of pine forests (4.5-km² scale) and a marginal, positive association with the area of pocosin habitat (0.3-km² scale). Bear density was not associated with pine forest at a smaller scale (0.3-km²), nor with major road density or an index of largest patch size. Predicted bear densities were low throughout the coastal region and only a few larger areas had high predicted densities, most of which were centered on public lands (e.g., Francis Marion National Forest, Lewis Ocean Bay). We sampled a third bear population in the Green Swamp area of North Carolina for genetic structure analyses and found no evidence of historic fragmentation among the 3 sampled populations. Neither did we find evidence of more recent barriers to gene exchange; with the exception of 1 recent migrant, Bayesian population assignment techniques identified only a single population cluster that incorporated all 3 sampled areas. Bears in the region may best be managed as 1 population. If the goal is to maintain or increase bear densities, demographic connectivity of high-density areas within the low-density landscape matrix is a key consideration and managers would need to mitigate potential impacts of planned highway expansions and anticipated development. Because the distribution of black bears in coastal South Carolina is not fully known, the regional map of potential black bear density can be used to identify focal areas for management and sites that should be surveyed for occupancy or where more intensive studies are needed. © 2012 The Wildlife Society.     

AFLP markers reveal high clonal diversity and extreme longevity in four key arctic-alpine species

We investigated clonal diversity, genet size structure and genet longevity in populations of four arctic‐alpine plants (Carex curvula, Dryas octopetala, Salix herbacea and Vaccinium uliginosum) to evaluate their persistence under past climatic oscillations and their potential resistance to future climate change. The size and number of genets were determined by an analysis of amplified fragment length polymorphisms and a standardized sampling design in several European arctic‐alpine populations, where these species are dominant in the vegetation. Genet age was estimated by dividing the size by the annual horizontal size increment from in situ growth measurements. Clonal diversity was generally high but differed among species, and the frequency distribution of genet size was strongly left‐skewed. The largest C. curvula genet had an estimated minimum age of c. 4100 years and a maximum age of c. 5000 years, although 84.8% of the genets in this species were <200 years old. The oldest genets of D. octopetala, S. herbacea and V. uliginosum were found to be at least 500, 450 and 1400 years old, respectively. These results indicate that individuals in the studied populations have survived pronounced climatic oscillations, including the Little Ice Age and the postindustrial warming. The presence of genets in all size classes and the dominance of presumably young individuals suggest repeated recruitment over time, a precondition for adaptation to changing environmental conditions. Together, persistence and continuous genet turnover may ensure maximum ecosystem resilience. Thus, our results indicate that long‐lived clonal plants in arctic‐alpine ecosystems can persist, despite considerable climatic change.     

A simulation study of the effects of architectural constraints and resource translocation on population structure and competition in clonal plants

(1) Spatially explicit simulation of clonal plant growth is used to determine how ramet-level traits affect ramet density, spatial pattern of ramets and competitive ability of a clonal plant. The simulation model used combines elements of (i) an individual-based model of plant interactions, (ii) an architectural model of clonal plant growth, and (iii) a model of resource translocation within a set of physiologically integrated plant individuals. (2) The effects of two groups of parameters were studied: growth and resource acquisition parameters (resource accumulation, density-dependence of resource accumulation, resource translocation between ramets) and architectural rules (branching angle and probability of branching, internode length). The model was parameterised by values approximating those of clonally growing grasses as closely as possible. The basic parameter values were chosen from a short-turf grassland. Sensitivity analysis was carried out on relevant parameters around three basic points in the parameter space. Both single-species and two-species systems were studied. (3) It is shown that increasing resource acquisition and growth parameters increase ramet density, genet number and competitive ability. Translocation parameters and architectural parameters modify the effects of resource acquisition and growth, but their effect in single-species stands was smaller. (4) The simulations of species with fixed ramet sizes showed that ramet density in single-species stands cannot be used for predicting competitive ability. Increase in resource acquisition and growth parameters was correlated with an increase in equilibrium ramet density and competitive ability. Increasing branching angle, branching probability or internode length lead to an increased competitive ability, but did not affect equilibrium ramet density. Change of architectural parameters could therefore affect competitive ability independently of their effect on the final ramet density. (5) Spatial pattern both in single-species and two-species stands was also highly parameter-dependent. Changes in architectural parameters and in translocation usually lead to pronounced change in the spatial pattern; change in growth and resource acquisition parameters generally had little effect on spatial pattern.     

The effect of branching angle on adaptive growth in patchy environments

In clonal plants, the genetic individual (genet) develops via the production of multiple physiological individuals (ramets). The branching angle between the ramets can significantly influence the shape of the developing clone. We investigated the adaptive significance of this phenomenon by means of a spatially explicit dynamic model of clonal growth. We studied the effect of the branching angle on the efficiency of filling habitat patches, varying the sizes and shapes of the patches. Two growth forms were compared: the Narrow Range (NR) versus Wide Range (WR) form. In the NR plant, the branching angle was always acute, while in the WR plant, both acute and wide angles could occur. We hypothesized that the NR plant would be less successful, as narrower branching constrained the plant’s ability to turn. The simulations revealed an opposite trend: the NR plant occupied more space in most of the simulated habitats. However, the effect was weak in general. We conclude that the branching angle between ramets is likely to be a neutral trait in terms of natural selection.     

Inbreeding depression influences genet size distribution in a marine angiosperm

Although inbreeding depression is a major genetic phenomena influencing individual fitness, it is difficult to measure in wild populations. An alternative approach is to correlate heterozygosity, measured using highly polymorphic markers, with a fitness-correlated trait. In clonal plants, genet size is predicted to be fitness correlated. Here we test the prediction that the genet size distribution of the marine clonal plant Zostera marina (eelgrass) is influenced by inbreeding depression. We used nine polymorphic microsatellite markers to access the fine scale clonal structure and to measure individual heterozygosity within 4 plots (each corresponds to 256 m2, sampled at 1-m intervals) in two populations along the German Baltic Coast. The same plots were also sampled for flowering and vegetative shoots to obtain estimates for sexual reproductive output at the level of the genetic individual. We found substantial differences in the genet size distribution between the two populations that may be explained by different disturbance frequency. In both populations, clone size was significantly positively correlated with the total number of flowering shoots, indicating that larger clones have a higher reproductive output. Individual heterozygosity was significantly positively associated with clone size. The effect was much stronger in Falkenstein (low disturbance) than in Maasholm (high disturbance). The results indicate that in a low disturbance population the relatively outbred clones occupy a higher proportion of the available space, possibly because they outcompete relatively inbred neighbours.     

Short‐term fitness benefits of physiological integration in the clonal herb Hydrocotyle peduncularis

Abstract We test whether physiological integration enhances the short‐term fitness of the clonal herb Hydrocotyle peduncularis (Apiaceae, R. Brown ex A. Richards) subjected to spatial variation in water availability. Our measures of fitness and costs and benefits are based on the relative growth rate of fragmented genets. Physiological integration over a gradient in soil moisture resulted in a highly significant net benefit to genet growth of 0.015 g g^?1 day^?1. This net benefit represents a significant enhancement of the average fitness of fragmented genets spanning the moisture gradient relative to the average of those growing in homogeneous moist or dry conditions. Sections of genet fragments growing in dry conditions in spatially heterogeneous treatments had significantly higher growth than the sections they were connected to that were growing in moist conditions. Within fragments, older (parent) sections growing in moist conditions experienced significant costs from connection to younger (offspring) sections growing in dry conditions. In contrast, offspring sections with ample water did not experience any costs when connected to parent sections growing in dry conditions. However, the net benefit of physiological integration was similar for parent and offspring sections, suggesting that parent and offspring sections contributed equally to the net benefit of physiological integration to genet growth and short‐term fitness.     

sGD: software for estimating spatially explicit indices of genetic diversity

Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans.     

Evaluating sampling designs by computer simulation: a case study with the Missouri bladderpod

To effectively manage rare populations, accurate monitoring data are critical. Yet many monitoring programs are initiated without careful consideration of whether chosen sampling designs will provide accurate estimates of population parameters. Obtaining accurate estimates is especially difficult when natural variability is high, or limited budgets determine that only a small fraction of the population can be sampled. The Missouri bladderpod, Lesquerella filiformis Rollins, is a federally threatened winter annual that has an aggregated distribution pattern and exhibits dramatic interannual population fluctuations. Using the simulation program SAMPLE, we evaluated five candidate sampling designs appropriate for rare populations, based on 4 years of field data: (1) simple random sampling, (2) adaptive simple random sampling, (3) grid-based systematic sampling, (4) adaptive grid-based systematic sampling, and (5) GIS-based adaptive sampling. We compared the designs based on the precision of density estimates for fixed sample size, cost, and distance traveled. Sampling fraction and cost were the most important factors determining precision of density estimates, and relative design performance changed across the range of sampling fractions. Adaptive designs did not provide uniformly more precise estimates than conventional designs, in part because the spatial distribution of L. filiformis was relatively widespread within the study site. Adaptive designs tended to perform better as sampling fraction increased and when sampling costs, particularly distance traveled, were taken into account. The rate that units occupied by L. filiformis were encountered was higher for adaptive than for conventional designs. Overall, grid-based systematic designs were more efficient and practically implemented than the others. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.