Preferred habitat and effective population size drive landscape genetic patterns in an endangered species

Landscape genetics provides a framework for pinpointing environmental features that determine the important exchange of migrants among populations. These studies usually test the significance of environmental variables on gene flow, yet ignore one fundamental driver of genetic variation in small populations, effective population size, N_e. We combined both approaches in evaluating genetic connectivity of a threatened ungulate, woodland caribou. We used least-cost paths to calculate matrices of resistance distance for landscape variables (preferred habitat, anthropogenic features and predation risk) and population-pairwise harmonic means of N_e, and correlated them with genetic distances, F_ST and D_c. Results showed that spatial configuration of preferred habitat and N_e were the two best predictors of genetic relationships. Additionally, controlling for the effect of N_e increased the strength of correlations of environmental variables with genetic distance, highlighting the significant underlying effect of N_e in modulating genetic drift and perceived spatial connectivity. We therefore have provided empirical support to emphasize preventing increased habitat loss and promoting population growth to ensure metapopulation viability.     

Micro- and macrorheological properties of isotropically cross-linked actin networks

Cells make use of semiflexible biopolymers such as actin or intermediate filaments to control their local viscoelastic response by dynamically adjusting the concentration and type of cross-linking molecules. The microstructure of the resulting networks mainly determines their mechanical properties. It remains an important challenge to relate structural transitions to both the molecular properties of the cross-linking molecules and the mechanical response of the network. This can be achieved best by well defined in vitro model systems in combination with microscopic techniques. Here, we show that with increasing concentrations of the cross-linker heavy meromyosin, a transition in the mechanical network response occurs. At low cross-linker densities the network elasticity is dominated by the entanglement length l_e of the polymer, whereas at high heavy meromyosin densities the cross-linker distance l_c determines the elastic behavior. Using microrheology the formation of heterogeneous networks is observed at low cross-linker concentrations. Micro- and macrorheology both report the same transition to a homogeneous cross-linked phase. This transition is set by a constant average cross-linker distance l_c ≈ 15 μm. Thus, the micro- and macromechanical properties of isotropically cross-linked in vitro actin networks are determined by only one intrinsic network parameter.     

How Levins’ dynamics emerges from a Ricker metapopulation model

Understanding the dynamics of metapopulations close to extinction is of vital importance for management. Levins-like models, in which local patches are treated as either occupied or empty, have been used extensively to explore the extinction dynamics of metapopulations, but they ignore the important role of local population dynamics. In this paper, we consider a stochastic metapopulation model where local populations follow a stochastic, density-dependent dynamics (the Ricker model), and use this framework to investigate the behaviour of the metapopulation on the brink of extinction. We determine under which circumstances the metapopulation follows a time evolution consistent with Levins’ dynamics. We derive analytical expressions for the colonisation and extinction rates (c and e) in Levins-type models in terms of reproduction, survival and dispersal parameters of the local populations, providing an avenue to parameterising Levins-like models from the type of information on local demography that is available for a number of species. To facilitate applying our results, we provide a numerical algorithm for computing c and e.     

Increasing frequency of low summer precipitation synchronizes dynamics and compromises metapopulation stability in the Glanville fritillary butterfly

Climate change is known to shift species'' geographical ranges, phenologies and abundances, but less is known about other population dynamic consequences. Here, we analyse spatio-temporal dynamics of the Glanville fritillary butterfly (Melitaea cinxia) in a network of 4000 dry meadows during 21 years. The results demonstrate two strong, related patterns: the amplitude of year-to-year fluctuations in the size of the metapopulation as a whole has increased, though there is no long-term trend in average abundance; and there is a highly significant increase in the level of spatial synchrony in population dynamics. The increased synchrony cannot be explained by increasing within-year spatial correlation in precipitation, the key environmental driver of population change, or in per capita growth rate. On the other hand, the frequency of drought during a critical life-history stage (early larval instars) has increased over the years, which is sufficient to explain the increasing amplitude and the expanding spatial synchrony in metapopulation dynamics. Increased spatial synchrony has the general effect of reducing long-term metapopulation viability even if there is no change in average metapopulation size. This study demonstrates how temporal changes in weather conditions can lead to striking changes in spatio-temporal population dynamics.     

Modelling single nucleotide effects in phosphoglucose isomerase on dispersal in the Glanville fritillary butterfly: coupling of ecological and evolutionary dynamics

Dispersal comprises a complex life-history syndrome that influences the demographic dynamics of especially those species that live in fragmented landscapes, the structure of which may in turn be expected to impose selection on dispersal. We have constructed an individual-based evolutionary sexual model of dispersal for species occurring as metapopulations in habitat patch networks. The model assumes correlated random walk dispersal with edge-mediated behaviour (habitat selection) and spatially correlated stochastic local dynamics. The model is parametrized with extensive data for the Glanville fritillary butterfly. Based on empirical results for a single nucleotide polymorphism (SNP) in the phosphoglucose isomerase (Pgi) gene, we assume that dispersal rate in the landscape matrix, fecundity and survival are affected by a locus with two alleles, A and C, individuals with the C allele being more mobile. The model was successfully tested with two independent empirical datasets on spatial variation in Pgi allele frequency. First, at the level of local populations, the frequency of the C allele is the highest in newly established isolated populations and the lowest in old isolated populations. Second, at the level of sub-networks with dissimilar numbers and connectivities of patches, the frequency of C increases with decreasing network size and hence with decreasing average metapopulation size. The frequency of C is the highest in landscapes where local extinction risk is high and where there are abundant opportunities to establish new populations. Our results indicate that the strength of the coupling of the ecological and evolutionary dynamics depends on the spatial scale and is asymmetric, demographic dynamics having a greater immediate impact on genetic dynamics than vice versa.     

A flow network model for animal movement on a landscape with application to invasion

Animal movement, whether for foraging, mate-seeking, predator avoidance, dispersal, or migration, is a fundamental aspect of ecology that shapes spatial abundance distributions, genetic compositions, and dynamics of populations. A variety of movement models have been used for predicting the effects of natural or human-caused landscape changes, invading species, or other disturbances on local ecology. Here we introduce the flow network—a general modeling framework for population dynamics and movement in a metapopulation representing a network of habitat sites (nodes). Based on the principles of physical transport phenomena such as fluid flow through pipes (Pouiselle’s Law) and analogously, the flow of electric current across a circuit (Ohm’s Law), the flow network provides a novel way of modeling movement, where flow rates are functions of relative node pressures and the resistance to movement between them. Flow networks offer the flexibility of incorporating abiotic and biotic conditions that affect either pressures, resistance, or both. To illustrate an application of the flow network, we present a theoretical invasion scenario. We consider the effects of spatial structure on the speed of invasion by varying the spatial regularity of node arrangement. In the context of invasion, we model management actions targeting nodes or edges, and consider the effects on speed of invasion, node occupation, and total abundance. The flow network approach offers the flexibility to incorporate spatial heterogeneity in both rates of flow and site pressures and offers an intuitive approach to connecting population dynamics and landscape features to model movement.     

Effective population size for culturally evolving traits

Population size has long been considered an important driver of cultural diversity and complexity. Results from population genetics, however, demonstrate that in populations with complex demographic structure or mode of inheritance, it is not the census population size, N, but the effective size of a population, N_e, that determines important evolutionary parameters. Here, we examine the concept of effective population size for traits that evolve culturally, through processes of innovation and social learning. We use mathematical and computational modeling approaches to investigate how cultural N_e and levels of diversity depend on (1) the way traits are learned, (2) population connectedness, and (3) social network structure. We show that one-to-many and frequency-dependent transmission can temporally or permanently lower effective population size compared to census numbers. We caution that migration and cultural exchange can have counter-intuitive effects on N_e. Network density in random networks leaves N_e unchanged, scale-free networks tend to decrease and small-world networks tend to increase N_e compared to census numbers. For one-to-many transmission and different network structures, larger effective sizes are closely associated with higher cultural diversity. For connectedness, however, even small amounts of migration and cultural exchange result in high diversity independently of N_e. Extending previous work, our results highlight the importance of carefully defining effective population size for cultural systems and show that inferring N_e requires detailed knowledge about underlying cultural and demographic processes.     

Long-term demographic surveys reveal a consistent relationship between average occupancy and abundance within local populations of a butterfly metapopulation

Species distribution models are the tool of choice for large-scale population monitoring, environmental association studies and predictions of range shifts under future environmental conditions. Available data and familiarity of the tools rather than the underlying population dynamics often dictate the choice of specific method – especially for the case of presence–absence data. Yet, for predictive purposes, the relationship between occupancy and abundance embodied in the models should reflect the actual population dynamics of the modelled species. To understand the relationship of occupancy and abundance in a heterogeneous landscape at the scale of local populations, we built a spatio-temporal regression model of populations of the Glanville fritillary butterfly Melitaea cinxia in a Baltic Sea archipelago. Our data comprised nineteen years of habitat surveys and snapshot data of land use in the region. We used variance partitioning to quantify relative contributions of land use, habitat quality and metapopulation covariates. The model revealed a consistent and positive, but noisy relationship between average occupancy and mean abundance in local populations. Patterns of abundance were highly variable across years, with large uncorrelated random variation and strong local population stochasticity. In contrast, the spatio-temporal random effect, habitat quality, population connectivity and patch size explained variation in occupancy, vindicating metapopulation theory as the basis for modelling occupancy patterns in fragmented landscapes. Previous abundance was an important predictor in the occupancy model, which points to a spillover of abundance into occupancy dynamics. While occupancy models can successfully model large-scale population structure and average occupancy, extinction probability estimates for local populations derived from occupancy-only models are overconfident, as extinction risk is dependent on actual, not average, abundance.     

A formula for the mean lifetime of metapopulations in heterogeneous landscapes

We present a formula for the mean lifetime of metapopulations in heterogeneous landscapes. This formula provides new insights into the effect of the spatial structure of habitat networks on metapopulation survival, with consequences for modeling, landscape evaluation, and metapopulation management. In the whole study, the spatially realistic metapopulation model of Frank and Wissel is taken as a basis. First, as a key result on the way toward the desired formula, it is shown that a simple nonspatial (Levins-type) model is able to reproduce the behavior of the complex spatial model considered regarding the mean lifetime, provided its parameters appropriately summarize all the relevant details of spatial heterogeneity. Second, the formula presented reveals how data from species and landscape have to be combined to estimate the survival chance of a metapopulation without having to run any simulation or to solve numerically any model equation. Third, by taking the formula as a basis, landscape measures are derived that allow dissimilar habitat networks to be evaluated, compared, and ranked in terms of their effect on metapopulation survival. Fourth, a combination of analytical, nonlinear regression as well as aggregation techniques was used to deduce the formula presented. The potential of these techniques for simplifying (meta)population models that are complex due to spatial heterogeneity is discussed.     

Spatial variation in branch size promotes metapopulation persistence in dendritic river networks

1. Despite years of attention, the dynamics of species constrained to disperse within riverine networks are not well captured by existing metapopulation models, which often ignore local dynamics within branches.
2. We develop a modelling framework, based on traditional metapopulation theory, for patch occupancy dynamics subject to local colonisation–extinction dynamics within branches and regional dispersal between branches in size-structured, bifurcating riverine networks. Using this framework, we investigate whether and how spatial variation in branch size affects species persistence for dendritic systems with directional dispersal, including one-way (up- or downstream only) and two-way (both up- and downstream) dispersal.
3. Variation in branch size generally promotes species persistence more obviously at higher relative extinction rate, suggesting that previous studies ignoring differences in branch size in real riverine systems might overestimate species extinction risk.
4. Two-way dispersal is not always superior to one-way dispersal as a strategy for metapopulation persistence especially at high relative extinction rate. The type of dispersal that maximises species persistence is determined by the hierarchical level of the largest, and hence most influential, branch within the network. When considering the interactive effects of up- and downstream dispersal, we find that moderate upstream-biased dispersal maximises metapopulation viability, mediated by spatial branch arrangement.
5. Overall, these results suggest that both branch-size variation and species traits interact to determine species persistence, theoretically demonstrating the ecological significance of their interplay.

     

Impact of Noise on Molecular Network Inference

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.     

Spatial scale,topography and thermoregulatory behaviour interact when modelling species’ thermal niches

The spatial scale at which climate and species’ occupancy data are gathered, and the resolution at which ecological models are run, can strongly influence predictions of species performance and distributions. Running model simulations at coarse rather than fine spatial resolutions, for example, can determine if a model accurately predicts the distribution of a species. The impacts of spatial scale on a model's accuracy are particularly pronounced across mountainous terrain. Understanding how these discrepancies arise requires a modelling approach in which the underlying processes that determine a species’ distribution are explicitly described. Here we use a process‐based model to explore how spatial resolution, topography and behaviour alter predictions of a species thermal niche, which in turn constrains its survival and geographic distribution. The model incorporates biophysical equations to predict the operative temperature (T_e), thermal‐dependent performance and survival of a typical insect, with a complex life‐cycle, in its microclimate. We run this model with geographic data from a mountainous terrain in South Africa using climate data at three spatial resolutions. We also explore how behavioural thermoregulation affects predictions of a species performance and survival by allowing the animal to select the optimum thermal location within each coarse‐grid cell. At the regional level, coarse‐resolution models predicted lower T_e at low elevations and higher T_e at high elevations than models run at fine‐resolutions. These differences were more prominent on steep, north‐facing slopes. The discrepancies in T_e in turn affected estimates of the species thermal niche. The modelling framework revealed how spatial resolution and topography influence predictions of species distribution models, including the potential impacts of climate change. These systematic biases must be accounted for when interpreting the outputs of future modelling studies, particularly when species distributions are predicted to shift from uniform to topographically heterogeneous landscapes.     

Isolation and some properties of extracellular d-glucosyltransferases and d-fructosyltransferases from Streptococcus mutans serotypes c, e, and f

Extracellular d-glucosyltransferases (GTase) and d-fructosyltransferases (FTase) were isolated from Streptococcus mutans IB (serotype c), B14 (e), and OMZ175 (f) by chromatofocusing, followed by hydroxyapatite column chromatography. The GTases isolated from serotypes c, e, and f are basic proteins (pI 7.4). The serotype c and e enzymes have two protein components having M_r 173 000 and 158 000 and the enzyme of the serotype f one component having M_r 156 000. The GTases of all the serotypes showed a K_m value for sucrose of 10–14mm and an optimum pH 5.5–6.0 for enzyme activity, and their activities were enhanced by the presence of primer Dextran T10. The α-d-glucans synthesized by the purified GTases are water soluble and primarily consist of (1→6)-α-d-glucosidic linkage (41–66 mol/100 mol) and α-d-(1→3,6)-branch linkage (6–20 mol/100 mol), but significant proportions of α-d-(1→3), α-d-(1→4), and α-d-(1→3,4) linkages (11, 6, and 14 mol/100 mol, respectively) were detected in the serotype c α-d-glucan. The isolated FTases of the serotypes c, e, and f are acidic enzymes (pI 4.6) and consist of two components having M_r 84 000 and 76 000 for the serotype c enzyme, and 106 000 and 84 000 for the serotypes e and f enzymes, respectively. The K_m value for sucrose was 6, 10, and 17mm for the serotypes c, e, and f enzymes, respectively, and the optimum pH of enzymic activity 5.5–6.0. Reactivity with Concanavalin A, susceptibility to acid hydrolysis, and paper chromatography of the hydrolyzates suggested that the water-soluble β-d-fructans synthesized by the purified FTases were of the inulin-type and had chemical structures somewhat different among the serotypes.     

Determinants of Genetic Structure in a Nonequilibrium Metapopulation of the Plant Silene latifolia

Population genetic differentiation will be influenced by the demographic history of populations, opportunities for migration among neighboring demes and founder effects associated with repeated extinction and recolonization. In natural populations, these factors are expected to interact with each other and their magnitudes will vary depending on the spatial distribution and age structure of local demes. Although each of these effects has been individually identified as important in structuring genetic variance, their relative magnitude is seldom estimated in nature. We conducted a population genetic analysis in a metapopulation of the angiosperm, Silene latifolia, from which we had more than 20 years of data on the spatial distribution, demographic history, and extinction and colonization of demes. We used hierarchical Bayesian methods to disentangle which features of the populations contributed to among population variation in allele frequencies, including the magnitude and direction of their effects. We show that population age, long-term size and degree of connectivity all combine to affect the distribution of genetic variance; small, recently-founded, isolated populations contributed most to increase F _ST in the metapopulation. However, the effects of population size and population age are best understood as being modulated through the effects of connectivity to other extant populations, i.e. F _ST diminishes as populations age, but at a rate that depends how isolated the population is. These spatial and temporal correlates of population structure give insight into how migration, founder effect and within-deme genetic drift have combined to enhance and restrict genetic divergence in a natural metapopulation.     

Effects of population characteristics and structure on estimates of effective population size in a house sparrow metapopulation

Effective population size (N_e) is a key parameter to understand evolutionary processes and the viability of endangered populations as it determines the rate of genetic drift and inbreeding. Low N_e can lead to inbreeding depression and reduced population adaptability. In this study, we estimated contemporary N_e using genetic estimators (LDNE, ONeSAMP, MLNE and CoNe) as well as a demographic estimator in a natural insular house sparrow metapopulation. We investigated whether population characteristics (population size, sex ratio, immigration rate, variance in population size and population growth rate) explained variation within and among populations in the ratio of effective to census population size (N_e/N_c). In general, N_e/N_c ratios increased with immigration rates. Genetic N_e was much larger than demographic N_e, probably due to a greater effect of immigration on genetic than demographic processes in local populations. Moreover, although estimates of genetic N_e seemed to track N_c quite well, the genetic N_e‐estimates were often larger than N_c within populations. Estimates of genetic N_e for the metapopulation were however within the expected range (<N_c). Our results suggest that in fragmented populations, even low levels of gene flow may have important consequences for the interpretation of genetic estimates of N_e. Consequently, further studies are needed to understand how N_e estimated in local populations or the total metapopulation relates to actual rates of genetic drift and inbreeding.     

Invasive plant integration into native plant–pollinator networks across Europe

The structure of plant–pollinator networks has been claimed to be resilient to changes in species composition due to the weak degree of dependence among mutualistic partners. However, detailed empirical investigations of the consequences of introducing an alien plant species into mutualistic networks are lacking. We present the first cross-European analysis by using a standardized protocol to assess the degree to which a particular alien plant species (i.e. Carpobrotus affine acinaciformis, Impatiens glandulifera, Opuntia stricta, Rhododendron ponticum and Solanum elaeagnifolium) becomes integrated into existing native plant–pollinator networks, and how this translates to changes in network structure.Alien species were visited by almost half of the pollinator species present, accounting on average for 42 per cent of the visits and 24 per cent of the network interactions. Furthermore, in general, pollinators depended upon alien plants more than on native plants. However, despite the fact that invaded communities received more visits than uninvaded communities, the dominant role of alien species over natives did not translate into overall changes in network connectance, plant linkage level and nestedness. Our results imply that although supergeneralist alien plants can play a central role in the networks, the structure of the networks appears to be very permeable and robust to the introduction of invasive alien species into the network.     

Metapopulation dynamics and biological invasions: a spatially explicit model applied to a freshwater snail

The spatial spread of invading organisms is a major contemporary concern. We focus here on invasions in inherently fragmented habitats, such as freshwater systems, and explore the usefulness of metapopulation models in this context. Maximum-likelihood methods allow the estimation of colonization and extinction rates, as functions of habitat patch sizes and positions, from time series of presence/absence data. This framework also provides confidence intervals of these estimates and hypotheses tests. We analyze a previously unpublished 12-year survey of the spread of the introduced snail Tarebia granifera in 47 Martinican rivers. Simple metapopulation models reproduce with reasonable accuracy several quantitative aspects of the invasion, including regional abundance, spatiotemporal structure, and site-by-site colonization dates. Sensitivity analysis reveals that the invasion sequence depended strongly on metapopulation size (number of sites) and spatial structure (distances among sites). The invasion history has also been accelerated by stochastic events, as illustrated by a large, central river that happened to be colonized very early and served as an invasion pool. Finally, we discuss the benefits of this approach for the understanding of invasions in fragmented landscapes.     

Spatial factors contribute to benthic diatom structure in streams across spatial scales: Considerations for biomonitoring

Diatoms are widely used in the biological monitoring of streams because they are strong responders to environmental change, but dispersal and spatial factors can play important and potentially confounding roles in the presence, absence, and abundance of species along with characterizing species–environment relationships. To examine how spatial factors affect diatom community structure and biomonitoring, multiple scales were sampled including the Western Allegheny Plateau (n = 58), Leading Creek watershed (n = 18), and the adjacent Shade River watershed (n = 21) in southeast Ohio. Partitioning of spatial, environmental, and spatially-structured environmental variation was conducted on diatom assemblages and on diatom metrics used in biomonitoring. At the regional scale, diatom assemblages and metrics had strong relationships with agricultural (e.g., significant correlations with nutrients, conductivity, and pasture/row crops in the watershed) and alkalinity gradients. Diatom assemblages and metrics in both watersheds were strongly associated with acid mine drainage (AMD) impacts, and when spatial factors were set as covariables in CCAs, relationships with AMD gradients became even stronger, indicating the need to consider how spatial factors could reduce the strength of diatom-environment relationships. Metrics calculated at all scales had very little variation explained exclusively by spatial factors, likely because multiple species are combined into a simplified metric that reduces the effects of species dispersal. Local environmental variables accounted for 57, 42, and 42% of the total variation explained (TVE), and spatial variables accounted for 28, 31, and 37% of the TVE in the regional, Leading Creek, and Shade River datasets, respectively. The amounts of variation in diatom assemblages explained solely by spatial factors at these scales were substantial and similar to what has been reported at continental, national, and large regional (Level I Omernik ecoregions) scales (approximately 1/3 of TVE). Although amounts of variation explained are similar across scales, processes underlying the spatial structure likely differ. In addition to describing ecological patterns, recognizing the potential influence of spatial factors could improve the identification and management of environmental problems at a range of scales, as well as aid in the development of new research questions and hypotheses aimed at exploring factors that could explain portions of the spatially explicit variation.